Elisa Gavard
email: elisa.gavard@univ-amu.fr

Johannes C Ziegler
email: johannes.ziegler@univ-amu.fr

The effects of semantic and syntactic prediction on reading aloud

Keywords: semantic prediction, syntactic prediction, reading aloud, word recognition

Semantic and syntactic prediction effects were investigated in a word naming task using semantic or syntactic contexts that varied between three and six words. Participants were asked to read the contexts silently and name a target word, which was indicated by a color change.

Semantic contexts were composed of lists of semantically associated words without any syntactic information. Syntactic contexts were composed of semantically neutral sentences, in which the grammatical category but not the lexical identity of the final word was highly predictable. When the presentation time of the context words was long (1200 ms), both semantically and syntactically related contexts facilitated reading aloud latencies of target words and syntactically related contexts produced larger priming effects than semantically related contexts in two out of three analyses. When presentation time was short (200 ms), however, syntactic context effects disappeared, while semantic context effects remained significant. Across the three experiments, longer contexts produced faster response latencies, but longer contexts did not produce larger priming effects. Results are discussed in the context of the extant literature on semantic and syntactic priming and more recent evidence suggesting that syntactic information constrains single word recognition.

The effects of semantic and syntactic prediction on reading aloud

The idea of the predictive brain has become a key concept in neuroscience [START_REF] Friston | Predictive coding under the free-energy principle[END_REF], cognitive science and psychology [START_REF] Hohwy | The predictive mind[END_REF][START_REF] Lupyan | Words and the World : Predictive Coding and the Language-Perception-Cognition Interface[END_REF] over the past decade. In the context of this theory, the brain could be viewed as a "prediction engine" rather than a passive receiver of data [START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive science[END_REF]. That is, it makes predictions about future events and seeks to minimize errors in these predictions, which is the basis for implicit and adaptive learning [START_REF] Grossberg | Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control[END_REF]. The key idea is that the brain proactively anticipates what will happen next [START_REF] Bar | Predictions : A universal principle in the operation of the human brain[END_REF][START_REF] Den Ouden | How Prediction Errors Shape Perception, Attention, and Motivation[END_REF][START_REF] Friston | The free-energy principle : A unified brain theory[END_REF][START_REF] Rao | Predictive coding in the visual cortex : A functional interpretation of some extra-classical receptive-field effects[END_REF] and this is seen as having two advantages in information processing. First, if an outcome conforms to expectations, the processing of that event might be facilitated because some of the features of an upcoming event might have been pre-activated (priming). Second, if an outcome does not conform to expectations, then the brain must update its model of that domain, which provides an opportunity for adaptive learning and the formation of rich and accurate representations.

This idea about the brain being a prediction engine has been rather influential in psycholinguistics, where prediction is seen as one of the cognitive mechanisms involved in language comprehension, language production and reading [START_REF] Chang | Becoming syntactic[END_REF][START_REF] Federmeier | A Rose by Any Other Name : Long-Term Memory Structure and Sentence Processing[END_REF][START_REF] Pickering | An integrated theory of language production and comprehension[END_REF][START_REF] Staub | The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation[END_REF]. For instance, it has been argued that effective language processing in children and adults results from efficient use of prediction in both word and speech perception [START_REF] Bar | Predictions in the Brain: Using our past to generate a future[END_REF][START_REF] Drake | Effects in production of word pre-activation during listening : Are listener-generated predictions specified at a speech-sound level?[END_REF][START_REF] Mani | Prediction during language processing is a piece of cake-But only for skilled producers[END_REF][START_REF] Pickering | Predicting while comprehending language : A theory and review[END_REF]. In word production, speakers constantly predict their own utterances for the purpose of speech planification and speech monitoring. Similarly, listeners predict future utterances as they listen [START_REF] Johnson | Prediction plays a key role in language development as well as processing[END_REF].

The influence of word predictability on reading has been extensively investigated in research on eye movements. [START_REF] Ehrlich | Contextual effects on word perception and eye movements during reading[END_REF] were the first to show that fixations were shorter and less frequent to highly predictable words than to less predictable words.

Moreover, when a word can be predicted from the preceding context, the eyes are relatively likely to skip over the word rather than directly fixate it. These effects have been replicated in dozens of eye movement experiments (for review, see [START_REF] Staub | The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation[END_REF]. Typically, predictability is operationalized in terms of a word's cloze probability [START_REF] Taylor | Cloze procedure": a new tool for measuring readability[END_REF], that is the proportion of subjects who provide a given word in an offline sentence completion task. For example, in the sentence "It was a windy day, the boy went outside to fly his …", "kite" has a higher cloze probability than "airplane" although both words perfectly fit the context. Indeed, predictability along with word frequency is one of the two main variables that affect eye movements in current computational models of eye movements in reading, such as E-Z Reader [START_REF] Reichle | Toward a model of eye movement control in reading[END_REF] and SWIFT [START_REF] Engbert | SWIFT: a dynamical model of saccade generation during reading[END_REF]. Word predictability also plays an important role in language processing models, in which processing difficulty is a function of a word's probability in its context [START_REF] Hale | A probabilistic Earley parser as a psycholinguistic model[END_REF][START_REF] Levy | Expectation-based syntactic comprehension[END_REF].

The influence of word predictability on reading and on-line language comprehension has also been studied using event-related brain potentials (for reviews, see [START_REF] Delong | Probabilistic word pre-activation during language comprehension inferred from electrical brain activity[END_REF][START_REF] Kutas | Thirty Years and Counting : Finding Meaning in the N400 Component of the Event-Related Brain Potential (ERP)[END_REF]. The N400, which is the brain's electrophysiological response to any potentially meaningful item, is especially large to nouns that do not meaningfully fit with their preceding contexts ("the pizza is on the shoe"; [START_REF] Kutas | Reading senseless sentences: Brain potentials reflect semantic incongruity[END_REF]. Even when there is no semantic mismatch and both target words fit the context ("airplane" versus "kite" in the above example), the N400 is strongly inversely related (~-.90) to the most expected target [START_REF] Kutas | Brain potentials during reading reflect word expectancy and semantic association[END_REF].

Note that most of the above research focused on semantic rather than syntactic predictions or a mix of both (i.e., cloze probability). In the sentence "it was a windy day, so the boy went outside to fly his __", people not only make a semantic prediction (the target word must be a flying object) but also a syntactic prediction (the target word must be a noun). Indeed, any time a semantic prediction is made, a syntactic prediction is generated as well [START_REF] Ferreira | Predicting syntactic structure[END_REF]. However, this does not work the other way around. That is, people can make a syntactic prediction without being able to make a semantic prediction. For example, if the sentence starts with "Those_", people would be able to predict a plural noun phrase, but it would be difficult to make a semantic prediction about the upcoming word (see [START_REF] Ferreira | Predicting syntactic structure[END_REF].

Both semantic and syntactic prediction effects have extensively been investigated using the priming paradigm, in which a single word prime influences the reading of a subsequent, related target word [START_REF] Becker | Semantic context effects in visual word recognition : An analysis of semantic strategies[END_REF]den Heyer et al., 1985;[START_REF] Forster | The Pros and Cons of Masked Priming[END_REF]. The seminal finding goes back to [START_REF] Meyer | Facilitation in recognizing pairs of words : Evidence of a dependence between retrieval operations[END_REF] who showed that the recognition of a target word (e.g., "nurse") was facilitated if was preceded by a highly associated priming word (e.g., "doctor"), rather than an unrelated prime (e.g., "chair"). Although there has been some discussion as to whether semantic priming effects require associative relations between primes and targets [START_REF] Fischler | Semantic facilitation without association in a lexical decision task[END_REF][START_REF] Lupker | Semantic priming without association : A second look[END_REF] and whether semantic priming can be obtained with short or subliminal primes [START_REF] Draine | Replicable Unconscious Semantic Priming[END_REF][START_REF] Fischler | Latency of Associative Activation in Memory[END_REF][START_REF] Hohenstein | Semantic preview benefit in eye movements during reading : A parafoveal fast-priming study[END_REF], it seems to be the case that pure semantic priming effects can be observed in both lexical decision and naming even at relatively short stimulus-onset asynchronies (SOAs) [START_REF] Perea | Associative and semantic priming effects occur at very short stimulus-onset asynchronies in lexical decision and naming[END_REF] suggesting that at least one part of the effect reflects the automatic "spreading of activation" to words that are semantically related to the primes [START_REF] Collins | A Spreading-Activation Theory of Semantic Processing[END_REF].

Much less research has been conducted on syntactic priming. In the seminal study by [START_REF] Goodman | The role of syntactic context in word recognition[END_REF], the authors used single words as primes (e.g., articles and pronouns) that unequivocally predicted the syntactic class of the target (i.e., noun and verb, respectively).

They found that lexical decision latencies to targets were significantly shorter when they were preceded by a syntactically appropriate word (e.g., my oven) compared to a syntactically inappropriate word (e.g., he oven). [START_REF] Seidenberg | Pre-and postlexical loci of contextual effects on word recognition[END_REF] replicated their study in both a lexical decision and naming task. They found significant syntactic priming effects only in lexical decision but not in naming. Finally, [START_REF] Sereno | Graphemic, Associative, and Syntactic Priming Effects at a Brief Stimulus Onset Asynchrony in Lexical Decision and Naming[END_REF] investigated orthographic, associative, and syntactic priming effects in both a lexical decision and naming using fast masked priming with a prime duration of only 60 ms. She found that primes that were orthographically similar or identical to targets facilitated both lexical decision and naming.

However, associatively or syntactically related primes facilitated only lexical decision but not naming. She argued that fast-acting semantic and syntactic priming affects a post-lexical decision stage that would be operative in lexical decision but not in naming. Together then, it seems that different processes underlie semantic and syntactic priming.

In the present study, we decided to move away from the classic priming paradigm and use longer contexts to build up stronger semantic and syntactic predictions. We also used a task that allowed us to dissociate semantic from syntactic contexts while investigating responses to exactly the same target words. Finally, we focused on naming rather than lexical decision because naming is not subject to post-lexical decision strategies (e.g., [START_REF] Seidenberg | Pre-and postlexical loci of contextual effects on word recognition[END_REF].

In our task, a succession of words (between 4 and 7) was presented on the screen and participants were asked to read the words silently until a color change indicated a target word that had to be read aloud as quickly as possible. To assess the effects of semantic prediction, we compared a context of semantically related nouns (cat -dograbbit -mouse) with a context of unrelated nouns (table -green -flower -mouse). To assess the effects of syntactic prediction for the same target words, we compared syntactically correct sentences (she -likes -thismouse) with syntactically incorrect scrambled sentences (this -likes -she -mouse). Predictive strength of the context was manipulated by using contexts that varied between three and six words. Experiments 1 and 2 used a rather long presentation time of the context words (1200 ms) to give more time for semantic and syntactic predictions to build up; Experiment 3 used a shorter presentation time (200 ms) to test semantic and syntactic prediction in more ecological reading conditions.

We obtained the semantic relatedness between our context words and between the context and target words using a common vector space model (word2vec, [START_REF] Mikolov | Exploiting Similarities among Languages for Machine Translation[END_REF][START_REF] Mikolov | Exploiting Similarities among Languages for Machine Translation[END_REF] trained on a large French database (WikiFr). To compute syntactic prediction strength, we calculated conditional probabilities for grammatical categories of targets words as a function of preceding contexts using the Universal Dependencies corpus [START_REF] De Marneffe | Automatic and strategic effects in semantic priming : An examination of Becker's verification model[END_REF]. This allowed us to create sentences with syntactically highly predictive target words, for which the semantic relatedness was extremely weak.

We expected related contexts to facilitate reading aloud compared to unrelated or scrambled context. Given that highly predictive semantic contexts made it possible to preactivate the precise target word, whereas highly predictive syntactic contexts could only preactivate the grammatical class of the target word, we expected stronger context effects for semantic as opposed to syntactic contexts. We also predicted that the size of the priming effects should vary as a function of context length and SOA with greater priming effects for longer contexts and longer SOAs.

Experiment 1

Method

Participants. We recruited 20 participants from Aix-Marseille University (Marseille, France), all French native speakers (13 women), reported normal or corrected-to-normal vision, no learning disorders (dyslexia) and no neurological or psychiatric disorders. They were between 19 and 23 years old (M = 21.8; SD = 1.5) and signed informed-consent forms prior to participation.

Design and Stimuli.

Semantic prediction. We first selected semantic contexts, for which we could find a relatively large number of semantically related words (e.g., flowers, animals, transportation etc.). We then calculated the semantic relatedness between all context words using a distributional vector space model of semantic associations trained on a very large number of words (Frwiki, 11GB, 914,601,321 tokens, DISCO, Kolb, 2008). This allowed us to select from these semantic contexts 80 target words that were the most predictive in a given context (average cosine value = .75). Note that vector space models compute semantic similarities rather than co-occurrences or associative relationships (for a discussion, see [START_REF] Günther | Vector-Space Models of Semantic Representation From a Cognitive Perspective : A Discussion of Common Misconceptions[END_REF]. For the same target words, we created 80 unrelated contexts matched in word length and frequency that had no semantic relatedness according to DISCO (average cosine value = .12).

Context length varied systematically with 20 target words per context length (3,4,5,6). Note that in terms of cosine similarities (semantic prediction), shorter context were as predictive as longer contexts (F(1,3) = 1.32, p = .27). An example is given in Table 1.

Table 1

Example of target words presented in a semantic or syntactic context, in a related or unrelated condition for one subject (length of 4 words).

Condition

Context

Related Unrelated Semantic catdograbbitmouse tablegreenflowercar Syntactic theywantacar thislikesshemouse Syntactic Prediction. For the syntactic condition, we constructed 80 sentences using the same target words as in the semantic condition. Importantly, the syntactic contexts were constructed such that the target word was highly predictive on a syntactic basis but not at all on a semantic basis (verified using the same vector space model as above). Syntactic predictability was calculated using the Universal Dependencies corpus (de [START_REF] De Marneffe | Automatic and strategic effects in semantic priming : An examination of Becker's verification model[END_REF]. For each target word and sentence length, we calculated conditional probabilities of predicting the grammatical class of the target word taken account the preceding context (P(target/context) >

.60). Eighty syntactically nonpredictive contexts were created by using the same target words but scrambling the words of the context such that syntactic prediction was close to zero (P(target/context) < .01). As in the semantic condition, context length varied systematically with 20 target words per context length (3,4,5,6). Note that in terms of probabilities (syntactic prediction), shorter context were as predictive as longer contexts (F(1,3) = .94, p = .43).

Experimental Design. As illustrated in Table 1, we had three factors that were fully crossed: condition (semantic vs. syntactic), context (related vs. unrelated) and context length (3, 4, 5, 6 words). To limit target repetition, we created two stimulus lists (for two groups of participants) such that a related target in the semantic condition would be an unrelated target in the syntactic condition and vice versa. This was done such that a given participant would not encounter the same target word twice in the same condition. Altogether, the experiment consisted of two lists of 160 trials composed of 80-word sequences in the semantic condition (40 related and 40 unrelated) and 80 sentences in the syntactic condition (40 related and 40 unrelated). Semantic and syntactic conditions were blocked, and their order of presentation was counterbalanced across subjects. In each condition, the word sequences or sentences were presented in random order to each participant. Participants were instructed to read all context words silently and read aloud the target word as quickly as possible. The presence of a target word was indicated by a color change.

Participants could not fully anticipate when exactly a target word appeared because the contexts varied in length. The experimenter registered the accuracy of the pronunciations using a Chronos® device. Responses were recorded using a microphone and response latencies (RTs)

were calculated as the differences between the onset of the target word presentation and the onset of the naming response. The onset of the naming response was automatically detected by the Eprime software and the onset tags were verified manually for each response. A session typically lasted for about 20 minutes.

Analyses. We used R (Version 4.0.2) and lme4 [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF] to perform a linear mixed-effects (LME) analysis with participants and items as random effects and condition, context, and length as fixed effects. Out of all 3200 observations (RTs), 339 were removed because of a dysfunction of the microphone. We then removed extreme values (i.e., RTs below 200 ms or above 4000 ms) and we further considered as outliers data points that were above or below 2.5 standard deviations from each individual participant's mean RT. Outliers were replaced by the cutoff RT corresponding to each participant's ± 2.5 standard deviation (see [START_REF] Hoaglin | Performance of Some Resistant Rules for Outlier Labeling[END_REF]. We report unstandardized regression coefficients (b), standard errors (SEs), and t-values. Fixed effects were considered significant if |t| was greater than 1.96 [START_REF] Baayen | Analyzing linguistic data: A practical introduction to statistics using R[END_REF]. Fixed effects, random effects, and random slopes were only included if they significantly improved the model's fit in a forward stepwise model selection procedure.

Results

Participants did not make any naming errors. Thus, accuracy was not further analyzed.

Because the RT distribution was slightly skewed, we log-transformed the RTs. This resulted in a perfectly normal RT distribution (Skewness: -.14, Kurtosis: -.21) [START_REF] Blanca | Skewness and Kurtosis in Real Data Samples[END_REF]. For the LME analysis, the final model included condition, context, length, and all interactions between these variables. As random effects, we included by-participant intercepts, random slopes for condition and random slopes for context by participants. The results showed a significant effect of context (b = 0.02, SE = 0.006, t = 3.14) but no effect of condition. Although the syntactic priming effect appeared larger than the semantic priming effect, the interaction between the effects of context and condition failed to reach significance (b = 0.01, SE = 0.01, t = 1.14). Contrast analysis confirmed that participants were faster at reading the target word following related than unrelated contexts in both the semantic (b = -0.02, SE = 0.003, t = -4.58)

and syntactic condition (b = -0.02, SE = 0.004, t = -6.31) (see Figure 1). There was also an effect of context length (b = -0.02, SE = 0.007, t = -3.3), which reflected the fact that participants were faster as the context was getting longer, but this effect did not interact with the context (related versus scrambled).

Discussion

In line with our hypotheses, we found a rather large and significant prediction effect for both conditions. Semantically and syntactically related contexts, as compared to unrelated contexts, facilitated reading aloud of single target words. Although the priming effect appeared stronger in the syntactic than the semantic condition, the interaction between the effects of context and condition was not significant. Furthermore, both types of contexts (semantic and syntactic) produced an increase in response latencies as a function of context length (the longer the context, the faster the responses). However, this effect occurred whether the context was related or scrambled, which suggests that the effect was due to general response preparation (i.e., the longer participants wait, the faster they get in responding to the target word).

Experiment 2

Experiment 2 was designed as a replication of Experiment 1 with two main differences.

First, we used the same target word in each of the four conditions (semantically related and unrelated, syntactically related and unrelated) for each participant. Second, we introduced a prediction error condition in the semantically and syntactically related condition. That is, on 20% of the trials, a semantically or syntactically related context was followed by a mismatching (incongruent) target word. All other aspects of the design and procedure were identical to those of Experiment 1.

Method

Participants. We recruited 20 participants (17 women) at Aix-Marseille University (Marseille, France). All were native speakers of French and reported normal or corrected-tonormal vision, no learning disorders (dyslexia) and no neurological or psychiatric disorders.

They were between 18 and 21 years old (M = 19.4; SD = 0.8) and signed informed consent forms prior to participation.

Design and Stimuli.

Because each target word was seen in all contexts by all participants, we selected 40 target words that were yoked with related and unrelated semantic and syntactic contexts (see Table 2 for examples). In addition, we added 8 sequences in each related condition where the target word violated the semantic or syntactic prediction (incongruous condition). Thus, in the semantic condition, we had 88 sequences composed of 40 semantically related targets, 40 unrelated targets, and 8 incongruous targets. The target words were identical in each condition, only the semantic context changed. The unrelated condition was made up of the same context words as the related condition, but they were randomly scrambled, controlling for word length and frequency. Analyses with the vector space model (DISCO) confirmed that related context had an average cosine value off .75, whereas unrelated context had an average cosine value off .12. Context length was evenly distributed with 22 targets per context length (3,4,5,6). Note that in terms of cosine similarities (semantic prediction), shorter context were also as predictive as longer contexts than in the Experiment 1.

Similarly, in the syntactic condition, we had 88 sentences made up of 40 syntactically correct, 40 incorrect (scrambled), and 8 incongruent sentences (see Table 2 for examples). The same target words were the same in each condition. As for the semantic condition, the unrelated context was composed of the same sentences as in the related context, but context words were scrambled such that conditional syntactic prediction probability (P(target/context)) was lower than .01. Context length was evenly distributed with 22 targets per context length (3,4,5,6).

Note that in terms of probabilities (syntactic prediction), shorter context were also as predictive as longer contexts than in the Experiment 1. The semantic and syntactic conditions were blocked, and the order was counterbalanced across participants. The trials were presented in a different random order for each participant. Procedure. The procedure was identical to Experiment 1.

Analyses. Analyses were identical to Experiment 1. Out of all 3520 observations (RTs), 243 were removed because of a dysfunction of the microphone. Extreme values were removed (i.e., RTs below 200 ms or above 4000 ms) and outliers were replaced by the cutoff RT corresponding to each participant's ± 2.5 standard deviation (see [START_REF] Hoaglin | Performance of Some Resistant Rules for Outlier Labeling[END_REF]. We report unstandardized regression coefficients (b), standard errors (SEs), and t-values. Fixed effects were considered significant if |t| was greater than 1.96 [START_REF] Baayen | Analyzing linguistic data: A practical introduction to statistics using R[END_REF]. Fixed effects, random effects, and random slopes were only included if they significantly improved the model's fit in a forward stepwise model selection procedure.

Results

Participants did not make any naming errors. Thus, accuracy was not further analyzed.

Because the RT distribution was slightly skewed, we log-transformed the RTs. This resulted in an acceptable normal RT distribution (Skewness: -.24, Kurtosis: 1.05) [START_REF] Blanca | Skewness and Kurtosis in Real Data Samples[END_REF]. We separated the analyses of the standard context by condition effect (as in Experiment 1) from the analysis of prediction errors. We then provide a combined analysis of Experiment 1 and 2.

Standard Analysis. This analysis was identical to that of Experiment 1. The final model included condition, context (related vs scrambled), length, and all interactions. As random effects, we included by-participant intercepts, random slopes for condition and random slopes for context by participants. The results showed a significant effect of context (b = 0.02, SE = 0.005, t = 3.79), no effect of condition, but a significant interaction between the effects of condition and context (b = 0.02, SE = 0.01, t = 2.01), which reflected the fact that the context effect was stronger for the syntactic than for the semantic condition. Contrast analyses confirmed that participants were faster at reading the target word following related than scrambled contexts in both the semantic (b = -0.01, SE = 0.005, t = -2.69) and syntactic condition (b = -0.02, SE = 0.007, t = -3.23) (see Figure 2). There was also an effect of context length (b = -0.03, SE = 0.009, t = -3.32), which reflected that participant were faster when the context was longer, but this effect did not interact with the context (related versus scrambled). between the fixed effects. There was an effect of length (b = -0.03, SE = 0.01, t = -2.95) which reflected that participant were faster when the context was longer, but this effect did not interact with the context (scrambled or incongruent).

Experiment and 2 combined. In the combined LME analyses, the final model included experiment (experiment 1 vs experiment 2), condition, context, length, and all interactions. As random effects, we included by-participant intercepts, random slopes for condition and random slopes for context by participants. Because the participants were faster in the second experiment (M = 565; SD = 110) than in the first (M = 641; SD = 158), we zscored the raw data for each subject to eliminate absolute speed differences between Experiment 1 and 2 (see [START_REF] Faust | Individual Differences in Information-Processing Rate and Amount : Implications for Group Differences in Response Latency[END_REF].The results showed a significant effect of context (b = 0.17, SE = 0.04, t = 4.79), no effect of condition but a significant interaction between the effects of condition and context (b = 0.22, SE = 0.07, t = 2.92). This interaction indicates that the context effect was stronger for the syntactic condition (effect size = .22) than for the semantic condition effect size = .13). Contrast analyses confirmed that participants were faster to read the target word for related than for scrambled contexts in the semantic condition (b = -0.13, SE = 0.03, t = -4.8) and in the syntactic condition (b = -0.22, SE = 0.03, t = -8.4). There was also an effect of length (b = -0.26, SE = 0.08, t = -3.29) which reflected that participant were faster when the context is longer, but this effect did not interact with the context (related versus scrambled). The results of the combined analysis are shown in Figure 3.

Discussion

As in Experiment 1, we replicated both semantic and syntactic prediction effects on reading aloud latencies using the exact same target words across all conditions in a withinsubject design. Unlike Experiment 1, we observed a significantly stronger context effect for the syntactic than the semantic condition. The combined analysis confirmed this result suggesting that the failure to find a significant context by condition interaction in Experiment 1, despite the apparent trend for larger context effects in the syntactic condition, was due to a lack of power. In contrast to our expectations, however, trials that induced prediction errors did not show additional costs compared to scrambled contexts. As in Experiment 1, we found context length effects for both conditions but again this effect occurred whether the context was related or unrelated, which suggest that the effects was due to response preparation.

Experiment 3

In the previous experiments, the presentation time for the context words was quite long (1200 ms). Such a long presentation time was chosen to make sure there was enough time to build strong expectations about upcoming target words. Thus, the question arose as to whether the prediction effects would persist if context words were presented for much shorter times (i.e., 200 ms). Indeed, [START_REF] Perea | Associative and semantic priming effects occur at very short stimulus-onset asynchronies in lexical decision and naming[END_REF] found that reliable semantic priming effects with rather short prime durations (SOA = 67 ms) and [START_REF] Snell | The sentence superiority effect revisited[END_REF] found strong syntactic effects on word identification (i.e., better partial report for words embedded in syntactically correct sequences) when 4-word sentences were presented for only 200 ms (see also [START_REF] Massol | The contribution of semantics to the sentence superiority effect[END_REF][START_REF] Wen | Fast syntax in the brain: Eletrophysiological evidence from the rapid parallel visual presentation paradigm (RPVP)[END_REF]. We therefore replicated Experiment 2 while reducing the presentation times of the context words to 200 ms.

Method

Participants. We recruited 21 participants (18 women) at Aix-Marseille University (Marseille, France). All were native speakers of French and reported normal or corrected-tonormal vision, no learning disorders (dyslexia) and no neurological or psychiatric disorders.

They were between 18 and 26 years old (M = 20,7; SD = 2,1) and signed informed consent forms prior to participation. Procedure. The procedure was identical to Experiment 1 and 2 except for the timing of the context words, that is, context words in both conditions were presented one by one in the center of the screen for 200 ms with a 100 ms ISI. The experiment lasted about 6 minutes.

Design and

Analyses. Analyses were identical to Experiment 1 and 2. Out of the 3360 observations (RTs), 89 were removed because of a dysfunction of the microphone. Extreme values were removed (i.e., RTs below 200 ms or above 4000 ms) and outliers were replaced by the cutoff RT corresponding to each participant's ± 2.5 standard deviation (see [START_REF] Hoaglin | Performance of Some Resistant Rules for Outlier Labeling[END_REF]. We report unstandardized regression coefficients (b), standard errors (SEs), and t-values. Fixed effects were considered significant if |t| was greater than 1.96 [START_REF] Baayen | Analyzing linguistic data: A practical introduction to statistics using R[END_REF]. Fixed effects, random effects, and random slopes were only included if they significantly improved the model's fit in a forward stepwise model selection procedure.

Results

Participants did not make any naming errors except for one participant who uttered the word "isocèle" as "isolé" in all four conditions. Those trials were removed. Otherwise, accuracy was not further analyzed. Because the RT distribution was slightly skewed, we log-transformed the RTs. This resulted in an acceptable normal RT distribution (Skewness: .7, Kurtosis: 3.2) [START_REF] Blanca | Skewness and Kurtosis in Real Data Samples[END_REF]. The final model included condition, context (related vs scrambled), length, and all interactions. As random effects, we included by-participant intercepts, random slopes for condition and random slopes for context by participants. The results showed a small effect but significant of context (b = 0.005, SE = 0.003, t = 1.92), but no effect of condition, nor an interaction between these two effects. Contrast analyses confirmed that priming effects were significant in the semantic condition (b = -0.008, SE = 0.004, t = -2.31) but not in the syntactic condition (b = -0.002, SE = 0.003, t = -0.47) (see Figure 4). There was also an effect of length (b = -0.03, SE = 0.007, t = -5.2) which reflected that participant were faster when the context is longer, but this effect did not interact with the context (related or scrambled).

Discussion

The results showed that the syntactic context effect disappeared with shorter SOAs while the semantic context effect remained significant. The effect size of the latter effect was smaller in Experiment 3 than the two previous experiments, which suggests that strong semantic and syntactic predictions need time to build up. As in Experiment 1 and 2, we found context length effects for both conditions but again this effect occurred whether the context was related or unrelated.

General discussion

Semantic priming effects have been previously reported in lexical decision and naming (see [START_REF] Neely | Semantic priming effects in visual word recognition: A selective review of current findings and theories[END_REF] for a review). However, syntactic priming effects have only been obtained in lexical decision but not naming [START_REF] Carello | Rapid naming is affected by association but not by syntax[END_REF][START_REF] Seidenberg | Pre-and postlexical loci of contextual effects on word recognition[END_REF][START_REF] Sereno | Graphemic, Associative, and Syntactic Priming Effects at a Brief Stimulus Onset Asynchrony in Lexical Decision and Naming[END_REF] except for a study by [START_REF] Farrar | Investigating Single-Word Syntactic Primes in Naming Tasks : A Recurrent NetworkApproach[END_REF] who obtained syntactic priming effects for high frequency words in naming when presenting primes and targets together ("her cars" versus "she cars").

The present study showed that syntactic priming effects can be found in reading aloud but only when context words were presented for a long time (1200 ms). Under these presentation conditions, syntactic priming was even bigger than semantic priming. However, syntactic priming effects disappeared when the context words were presented more swiftly (200 ms), while semantic priming effects remained significant, although somewhat smaller in size.

This result suggests that syntactic constraints on reading aloud require time to build up.

The finding is consistent with our initial assumption that the syntactic context does not allow to pre-activate a specific target word but only its grammatical class, whereas semantic contexts allow to pre-activate specific target words. It seems that short SOAs tap this automatic preactivation phase of individual target words. Syntactic constraints cannot pre-activate individual target words but they can considerably reduce the search space. For example, syntactic constraints make certain continuations strictly impossible ("He wants to → table"), whereas semantic constraints make them only highly implausible ("He pets his → table "). However, our data suggest that this process (i.e., reduction of search space) needs time to affect reading aloud responses to individual words.

Our results are somewhat different from those of Snell and Grainger (2017) who found strong syntactic effects (i.e., faster partial report word identification accuracy for words that were embedded in syntactically correct sentences as opposed to scrambled agrammatical sequences) when 4-word sequences were presented for only 200 ms in a rapid parallel visual presentation (RPVP). However, there is a notable difference between their and our experiments.

In Snell and Grainger, the 4-word sequences were presented all at once for only 200 ms, they were backward-masked, and participants had to make a partial report of one of the words. Thus, we are clearly in an "iconic memory" situation [START_REF] Sperling | The information available in brief visual presentations[END_REF], where the contents of brief alphanumeric displays are initially held in a high-capacity fast-decay visual-information store ("iconic memory"). In [START_REF] Sperling | The information available in brief visual presentations[END_REF] famous partial report experiments, it has been shown that partial-report performance decreases until it finally reaches the level of whole report at cue delays of about 500-800 ms. According to [START_REF] Coltheart | Iconic memory[END_REF], "the ability to report material from brief visual displays is seen as depending upon parallel (and perhaps unlimited) transfer from iconic memory to a post-categorical memory mode, followed by a limited (and perhaps serial) transfer to an output stage" (p. 283). It seems plausible that in Snell and Grainger's experiments syntactic information provides important constraints about which items are transferred from the rapidly decaying iconic memory into a longer lasting storage or output stage. This is very different from our experiment, in which context words were presented one by one for 200 ms followed by the target word. Clearly, the serial presentation mode of our experiment does not put any constraints on iconic memory. Despite these differences, both experiments show that syntactic constraints can affect word identification at various stages and time scales in conditions that minimized any potential influence of between-word semantic relatedness or predictability (i.e., cloze probability measures).

Coming back to our experiments, we would argue that semantically related contexts make it possible to make specific, discrete predictions of the upcoming target words. Given that we find semantic priming effects in a (non-semantic) naming task and at short SOAs, we believe that the pre-activation of a potential upcoming word facilitates letter and word identification rather later stages of lexical access. This is also in line with the observation that, in eye movements, predictability influences very early eye movement measures, including word skipping, which is the earliest possible measure [START_REF] Staub | The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation[END_REF]. Syntactically predictive contexts, however, cannot pre-activate a specific word. They can only broadly activate words that are likely to appear in the input or exclude or inhibit categories of words that are impossible to occur, thus considerably reducing the lexical search space. This is in line with the observation that predictability effects are present even at the low end of the cloze probability range.

According to [START_REF] Staub | The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation[END_REF], "it seems unlikely that readers would specifically predict a word that has relatively low cloze probability" (p. 322).

Note that these explanations would assume that the locus of semantic and syntactic context effects is different, and it would be interesting to investigate to what extent they can be separated in such a tightly controlled paradigm. Indeed, previous research suggested that semantic and syntactic priming mechanisms can be dissociated. [START_REF] Kutas | Reading senseless sentences: Brain potentials reflect semantic incongruity[END_REF] initially observed a N400 in response to a semantically incongruent word placed at the end of a sentence and in syntactically unrelated word pairs [START_REF] Bentin | Event-related potentials, lexical decision and semantic priming[END_REF]. In contrast, syntactic violations seem to generate P600 components [START_REF] Friederici | The Role of Left Inferior Frontal and Superior Temporal Cortex in Sentence Comprehension : Localizing Syntactic and Semantic Processes[END_REF][START_REF] Osterhout | Event-related brain potentials elicited by syntactic anomaly[END_REF].

Finally, [START_REF] Dapretto | Form and Content : Dissociating Syntax and Semantics in Sentence Comprehension[END_REF] indicated that part of Broca's area and the inferior part of the left inferior frontal gyrus are involved in the processing of syntactic information and semantic aspects of a sentence, respectively. However, the claim that the locus of semantics and syntax can be dissociated at the neural level is still a matter of considerable debate [START_REF] Siegelman | An Attempt to Conceptually Replicate the Dissociation between Syntax and Semantics during Sentence Comprehension[END_REF]. The design of our study is ideal to examine a dissociation at the electrophysiological or neural level because the last word is identical in all conditions, which allows one to investigate the locus and time course of syntactic versus semantic prediction in a more controlled fashion.

Context length had a strong effect on latencies (the longer the context, the faster the response latencies) but context length did not interact with context (related versus unrelated).

That is, we did not find that context length increased the size of the semantic or syntactic priming effects. This result suggests that context length only affected response preparation. We initially predicted that longer contexts should make semantic and syntactic predictions stronger thus producing bigger priming effects. It therefore seems that a three-word context is sufficient to generate strong semantic and/or syntactic expectations. Note that in terms of conditional probabilities (syntactic prediction) and cosine similarities (semantic prediction), shorter contexts were as predictive as longer contexts (see Methods).

Altogether our results are consistent with the concept of a "predictive brain" [START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive science[END_REF]). An aim for future research is to test these prediction effects at various time scales in a larger sample while adding measures of reading skill and statistical learning to further investigate whether semantic and syntactic prediction skills predict reading skills and whether genuinely predictive or anticipatory processes (e.g., statistical learning) predict linguistic prediction skills [START_REF] Arciuli | Reading as Statistical Learning[END_REF][START_REF] Hung | Domain Generality and Specificity of Statistical Learning and its Relation with Reading Ability[END_REF][START_REF] Isbilen | Individual differences in artificial and natural language statistical learning[END_REF].

Procedure.

  All participants agreed to take part in the study by filling out a consent form after the experimenter had explained the course of the study. The experiment was programmed with E-Prime 2.0. Stimuli were presented on a computer screen. Each trial began with the presentation of a fixation cross in the center of the screen for 1000 ms. Context words in both conditions were presented one by one in the center of the screen for 1200 ms with a 100 ms inter-stimuli-interval (ISI). The target word remained on the screen until the participant read it out loud. The next trial started after an inter-trial-interval (ITI) of 350 ms. The words were displayed in the center of the screen, in lowercase 30-point Arial font. Participants received instructions verbally by the experimenter and visually on the screen.

Figure 1

 1 Figure 1 Mean reaction times (ms) as a function of context (related vs scrambled) and condition (semantic vs syntactic) in Experiment 1. Errors bars indicate within-participant standard errors.

Figure 2

 2 Figure 2 Mean reaction times (ms) in Experiment 2. Panel A presents the classic analysis with context (related vs scrambled) and condition (semantic vs syntactic) as factors. Panel B presents the prediction error analysis with context (scrambled vs incongruent) and condition (semantic vs syntactic) as factors. Errors bars indicate within-participant standard errors.

Figure 3

 3 Figure 3 Combined analysis of Experiments 1 and 2 (z-score-transformed data) with context (related vs scrambled) and condition (semantic vs syntactic) as factors. Error bars show within-participant standard errors.

  Stimuli. Design and stimuli were identical to Experiment 2, each target word was seen in all contexts by all participants, but we have removed the incongruent condition. Participants saw 160 target words: 80 in the semantic condition (40 in a related context and 40 in an unrelated context) and 80 in the syntactic condition (same pattern).

Figure 4

 4 Figure 4 Mean reaction times (ms) in Experiment 3 as a function of context (related vs scrambled) and condition (semantic vs syntactic). Errors bars show within-participant standard errors.

Table 2

 2 Example

		Condition
	Context	Related	Unrelated	Incongruent
	Semantic	cat -dog -rabbit -mouse	table -green -flower -	blue -red -yellow -
			mouse	mouse
	Syntactic	she -likes -this -mouse	this -likes -she -mouse The -dog -comes -
				mouse

of target words presented in a semantic or syntactic context, in a related, unrelated, or incongruent condition for one subject (length of 4 words).

Acknowledgments

The authors declare that there is no conflict of interest. Both authors approved the final version of the manuscript. This research has been supported by the Institute of Convergence ILCB (ANR-16-CONV-0002) and the Excellence Initiative of Aix-Marseille University A*MIDEX (ANR-11-IDEX-0001-02). The work that led to this publication has also been supported by AMPIRIC, a center of excellence on research in education and teacher training.


Renseignements sur le projet

INFORMATIONS SUR LES DONNEES -Qualité des données

Responsable(s)

Marie DEBACQ (pilote du projet, responsable de la rédaction et de la mise à jour du PGD, responsable du dataverse "Rotary kilns and drums" , responsable des sauvegardes) Stéphane VITU pour le Cnam (superviseur des expériences sur le four tournant, responsable de la sauvegarde sur disque dur stockée au Cnam) Kevin LACHIN pour AgroParisTech (développeur des outils de calculs pour l'analyse des données, relecteur du PGD) Artemio PLANA-FATTORI (superviseur des simulations DEM)

Nommage des variables le cas échéant

Voir nomenclature qui sera fournie en annexe, sera également disponible dans l'entrepôt de données, ainsi que les publications associées.

Référentiel(s) de données (taxonomiques, géographiques, etc.)

Il n'en existe pas à notre connaissance.

Méthode(s) de contrôle de la qualité des données

INFORMATIONS SUR LES DONNEES -Métadonnées

Responsable(s) de l'attribution et de la mise à jour Marie DEBACQ, Kevin LACHIN

Standard(s)

Il n'en existe pas applicable à ces expériences, à notre connaissance.

Voir nomenclature qui sera fournie en annexe.

Thésaurus/ontologie

Il n'en existe pas applicable à ces expériences, à notre connaissance.

Localisation des métadonnées

Pour chaque expérience : un fichier de métadonnées, déposé au même endroit que le fichier de données, avec le même nom + l'extension "_meta".

MESURES LEGALES, REGLEMENTAIRES ET ETHIQUES

Données ouvertes

Elles ont vocation à le devenir sur Recherche Data Gouv > Data INRAE > Experimental -Observation -Simulation Dataverse > Rotary kilns and drums.

Lien direct : https://entrepot.recherche.data.gouv.fr/dataverse/rkd

FONCTIONNEMENT ET INFRASTRUCTURES -Stockage

Responsable(s)

DSN AgroParisTech pour la mise à disposition et la maintenance du stockage en ligne SeaFile. Marie DEBACQ pour la supervision du dépôt régulier par l'ensemble des contributeurs dans l'espace dédié sur SeaFile (nommé "RKD -RotaryKilnsAndDrums").

Méthode(s), supports

Les données produites sont stockées immédiatement après les expériences sur l'ordinateur des chercheurs, doctorants ou stagiaires qui les produisent. Elles sont régulièrement déposées par eux dans un Groupe dédié sur l'instance SeaFile d'AgroParisTech (groupe "RKD -

  studying heat transfer in externally heated rotary kilns : 10.1002/ceat.201000391 Transverse motion of cohesive powders in flighted rotary kilns: experimental study of unloading at ambient and high temperatures : 10.1016/j.powtec.2013.04.007 A hydrodynamic model for flighted rotary kilns used for convertion of cohesive uranium powders : 10.1016/j.ces.2013.09.037 Effect of Lifters Shape and Operating Parameters on the Flow of Materials in a Pilot Rotary Kiln: Part I. Experimental RTD and Axial Dispersion Study : 10.1016/j.powtec.2014.03.066 Effect of lifter shape and operating parameters on the flow of materials in a pilot rotary kiln: Part II. Experimental hold-up and mean residence time modeling : 10.1016/j.powtec.2014.05.070 Effect of lifter shape and operating parameters on the flow of materials in a pilot rotary kiln: Part III. Up-scaling considerations and segregation analysis : 10.1016/j.powtec.2016.04.052 Wall-to-solid heat transfer coefficient in flighted rotary kilns: experimental determination and modeling : 10.1016/j.expthermflusci.2017.10.024

DMP du projet "Fours et tambours tournants -Rotary kilns and drums" 1. INFORMATIONS SUR LES DONNEES -Origine(s) des données Residence Time Distribution Données provenant de sources extérieures néant Données produites dans le projet Distribution des Temps de

	Titre du projet	Fours et tambours tournants -Rotary kilns and drums
	Acronyme	RKD
	Résumé	étude expérimentale et modélisation des fours et tambour tournants
	Date de début	2007-10-01
	Partenaires		Paris-Saclay Food and Bioproduct Engineering Research Unit
			(202023536A)
			CENTRE DE RECHERCHE D'ALBI EN GENIE DES PROCEDES DES
			SOLIDES DIVISES, DE L'ENERGIE ET DE L'ENVIRONNEMENT
			(201220448D)
			CENTRE D' ÉNERGÉTIQUE ET DE THERMIQUE DE LYON
			(199911704F)
			Laboratoire de Chimie moléculaire, génie des procédés chimiques et
			énergétiques [CMGPCE] (201521310C)
			Laboratoire Génie des Procédés pour l'Environnement, l'Energie et la
			Santé [LGP2ES] (199212983B)
			Conservatoire National des Arts et Métiers [CNAM] ()
			INRAE ()
			AgroParisTech ()
	Produits de recherche :
	1. Residence Time Distribution (Jeu de données)
	2. Transverse hydrodynamics (Jeu de données)
	3. Heat transfer (Jeu de données)
	Contributeurs
	Nom		Affiliation	Rôles
				Coordinateur du projet
	DEBACQ Marie -0000-0002-0618-0810		Paris-Saclay Food and Bioproduct Engineering [SayFood]	Personne contact pour les données (RTD, Transverse, Thermal) Responsable du plan de gestion de
				données
	LACHIN Kevin -0000-0002-7549-0425	Paris-Saclay Food and Bioproduct Engineering [SayFood]
	PLANA-FATTORI Artemio -0000-0001-8326-7007	Paris-Saclay Food and Bioproduct Engineering [SayFood]
	VITU Stéphane -0000-0002-	Conservatoire National des Arts
	5442-1279		et Métiers [CNAM]

Séjour (DTS) = Residence Time Distribution (RTD)

Mesures réalisées sur les fours tournants, à température ambiante, avec un écoulement continu et en régime permanent du granulaire dans le four, sans circulation de gaz. Suivi de la concentration en traceur solide en sortie de four en fonction du temps, après injection impulsion d'une quantité connue de ce traceur en entrée du four => obtention de la courbe de distribution des temps de séjour => calcul des caractéristiques de cette distribution : temps de séjour moyen coefficient de dispersion nombre de Péclet

Objectif(s)/Utilisation(s) de chaque jeu de données dans le projet

Détermination des paramètres mentionnés dans la rubrique précédente, en vue d'être utilisés dans un modèle phénoménologique du four tournant + comparaison éventuelle des observations expérimentales avec les résultats de simulations DEM (Discret Element Method) ou CFD (Computational Fluid Dynamics).

Protocole(s) de création des jeux de données

  Le protocole est décrit dans les publications, en particulier : 10.1016/j.powtec.2014.03.066 (hal-00974052), ainsi que la thèse d'Alex Stéphane Bongo Njeng. Voir également documents expliquant ces protocoles dans l'entrepôt de données. Les liens pérennes vers ces documents seront donnés ici dès que disponibles.

	Outils

spécifiques pour lire les données néant Transverse hydrodynamics Données provenant de sources extérieures néant Données produites dans le projet Hydrodynamique transverse

  Mesures réalisées sur les tambours tournants, à température ambiante. Réalisation de films pendant la rotation du tambour, préalablement chargé avec une quantité connue de granulaire => extraction d'images de ces films => détermination par analyse de ces images des paramètres suivants :

	angle de talus dynamique
	épaisseur de couche active
	degré de mélange au cours du temps
	Objectif(s)

/Utilisation(s) de chaque jeu de données dans le projet

  Détermination des paramètres mentionnés dans la rubrique précédente, en vue d'être utilisés dans un modèle phénoménologique du four tournant + comparaison des observations expérimentales avec les résultats de simulations DEM (Discret Element Method).

Protocole(s) de création des jeux de données

Voir documents expliquant ces protocoles dans l'entrepôt de données. Les liens pérennes vers ces documents seront donnés ici dès que disponibles.

Outils

spécifiques pour lire les données néant Heat transfer Données provenant de sources extérieures néant Données produites dans le projet Transferts thermiques

Mesures réalisées sur les fours tournants, au cours d'un chauffage ou d'un refroidissement.

Suivi des températures dans la poudre, dans le gaz et à proximité immédiate de la paroi extérieure du four, en fonction du temps et à diverses position dans le pilote => écriture du bilan thermique en régime transitoire pour déterminer par identification : le coefficient de transfert thermique paroi/poudre Objectif(s)/Utilisation(s)

de chaque jeu de données dans le projet

  Détermination du paramètre mentionné dans la rubrique précédente, en vue d'être utilisé dans un modèle phénoménologique du four tournant.

	Protocole(s)

de création des jeux de données

  Le protocole est décrit dans les publications, en particulier : 10.1016/j.expthermflusci.2017.10.024 (hal-01624450), ainsi que la thèse d'Alex Stéphane Bongo Njeng. Voir également documents expliquant ces protocoles dans l'entrepôt de données. Les liens pérennes vers ces documents seront donnés ici dès que disponibles.

	Outils

spécifiques pour lire les données néant

Responsable(s) du respect du cadre légal

  Marie DEBACQ, en lien avec les juristes des tutelles au sein desquelles ces travaux ont été menés.Aucune donnée n'impliquant des manipulations animales ou humaines, le cas ne se présente pas.

	néant à ce jour (juillet 2022)
	Cadre éthique et déontologique
	Données personnelles
	non concerné
	Données issues de ressources génétiques
	non
	Données confidentielles
	non (plus maintenant, en juillet 2022)
	Titularité des droits sur les données, propriété intellectuelle
	Les données produites entre 2012 et 2015 (dans le cadre de la thèse d'Alex Stéphane BONGO NJENG) sont la copropriété du Cnam, du Centre
	RAPSODEE et du CETHIL.
	Celles produites entre octobre 2021 et juillet 2022 (dans le cadre du projet de 3e année de Maud LESCROART et Paul FOURRE et du stage de M1
	d'Aka DOCHE) sont la copropriété du Cnam et d'AgroParisTech.
	Partenariats internationaux

Celles produite entre septembre 2022 et décembre 2023 (dans le cadre du post-doctorat de Marcela V. C. MACHADO) sont la copropriété du Cnam, d'AgroParisTech et d'INRAE.

Répétition des expériences. Calcul de coefficients de régression. Tracé de droites de corrélation.

Nous continuons d'utiliser nos anciennes données pour étendre l'assise expérimentale de nos travaux de modélisation et ainsi améliorer les performances de nos modèles. Le développement de notre thématique de recherche prévoit par ailleurs de confronter ces données expérimentales à des résultats de simulations (en particulier par DEM). Ces données sont également susceptibles d'être réutilisées par nos soins ou par d'autres chercheurs, avec de nouvelles méthodologies d'analyse ou de modélisation en devenir. Enfin nous prévoyons d'écrire aux chefs des groupes de recherche que nous connaissons travaillant sur cette thématique, pour les informer de la publication de nos données ; en les invitant à les utiliser, par exemple pour les comparer avec leurs propres données expérimentales ou issues de simulations.

ARCHIVAGE Responsable(s)

Marie DEBACQ

Périmètre

Ce projet a vocation a se poursuivre pendant encore plusieurs années. Il n'est pas lié à un financement spécifique avec une date précise de fin. Nous n'envisageons donc pas d'archivage pour le moment.

Volume

Infrastructure

Question sans réponse.

Financement

Question sans réponse.


Pierre Larmande

Aravind Venkatesan

Gildas Tagny

Quan Do

Bertrand Pitollat

Ndomassi Tando

Konstantin Todorov

Yann Pomie

Manuel Ruiz

The AgroLD project : A Knowledge Graph Database for plant functional genomics

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The AgroLD project : A Knowledge Graph Database for plant functional genomics

@agro ld

Pierre Larmande 1,3,4 , Aravind Venkatesan, Gildas Tagny, Quan Do, Bertrand Pitollat 2,3 , Ndomassi Tando

RDF Transformation workflows

We are open for discussions and collaborations. Feel free to contact us:

Dr. Pierre Larmande: pierre.larmande@ird.fr

Networks and supports

The Advanced Search query form is based on the REST API suite, developed under the AgroLD project. The aim of this effort is to provide non-technical users with a tool to query the knowledge base.

AgroLD

Cacao

The SPARQL Query Editor provides an interactive environment to formulate SPARQL queries.


Pierre Larmande
email: pierre.larmande@ird.fr

Bertrand Pitollat

Ndomassi Tando

Yann Pomie

Bill Happi

Valentin Guignon

Manuel Ruiz

AgroLD: a Knowledge Graph for the Plant Sciences

Keywords: Knowledge Graph, Linked Data, FAIR data, Plant Sciences, Bioinformatics 1

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Montpellier and the South of France. The online documentation provides an overview of the integrated data sources2 . The conceptual framework of AgroLD is based on well-established ontologies in the plant field such as Gene Ontology, Plant Ontology or Plant Trait Ontology. Furthermore, we developed a dedicated schema3 that creates links between the imported ontologies and introduces new classes and properties. The online documentation shows the complete list of the used ontologies. The majority of these ontologies are hosted by the OBO Foundry project.

Statistics

As of today, AgroLD contains more than 900 Millions triples resulting of the integration of roughly 100 datasets gathered in 33 named graphs. Table 1 gives an overview of available resources and tools. All datasets are available in Zenodo under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Each resource can contain several datasets, for instances, one dataset per species or per data type. Combining all ontologies and datasets imported, AgroLD graph gather 383 classes and 793 properties. Among the pipelines developed to lift up the datasets, we focused also on connecting our datasets with others. The property rdfs:seeAlso reach the total number of almost 80 millions of outbound links making the AgroLD graph correctly linked with other datasets in the LOD. Besides, we paid attention to increasing the number of semantic annotations with imported ontologies, which increased the number of links between datasets making the overall graph denser. We created more than 14 million semantic links linking entities to ontological classes. Finally, our data linking strategy allowed us to create around 160,000 owl:sameAs links between entities.

Table 1 Links to AgroLD resources and tools

Name of resource or tool and description, URL

http://www.agrold.org/documentation.jsp

https://github.com/SouthGreenPlatform/AgroLD_ETL/tree/master/model

Data


domain: sde phys

text:
Chao Tian

Kun Du

Lixin Wang
email: lxwang@iupui.edu

Xiao Zhang

Fadong Li

Wenzhe Jiao

Daniel Beysens

Kudzai Farai Kaseke

Marie-Gabrielle Medici

Stable isotope variations of dew under three different climates

As a supplementary or the only water source in dry regions, dew plays a critical role in the survival of organisms. The new hydrological tracer 17 O-excess, with almost sole dependence on relative humidity, provides a new way to distinguish the evaporation processes and reconstruct the paleoclimate. Up to now, there is no published daily dew isotope record on δ 2 H, δ 18 O, δ 17 O, d-excess, and 17 O-excess. Here, we collected daily dew between July 2014 and April 2018 from three distinct climatic regions (i.e., Gobabeb in the central Namib Desert with desert climate, Nice in France with Mediterranean climate, and Indianapolis in the central United States with humid continental climate). The δ 2 H, δ 18 O, and δ 17 O of dew were simultaneously analyzed using a Triple Water Vapor Isotope Analyzer based on Off-Axis Integrated Cavity Output Spectroscopy technique, and then d-excess and 17 O-excess were calculated. This report presents daily dew isotope dataset under three climatic regions. It is useful for researchers to use it as a reference when studying global dew dynamics and dew formation mechanisms.

Background & Summary

Global warming has increased the demand of moisture in the local atmosphere, leading to a decrease in precipitation over many regions, both of which could contribute to drought [START_REF] Dai | Climate Change and Drought: a Precipitation and Evaporation Perspective[END_REF][START_REF] Jiao | Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future[END_REF] . Water vapor can condense as dew on a surface where radiation cools below the dew point temperature 3,[START_REF] Monteith | Principles of environmental physics: plants, animals, and the atmosphere[END_REF] . Dew, as a significant source of non-rainfall water, is considered a vital water source due to its considerable contribution for the surface water balance, especially in semiarid and arid regions 3,[START_REF] Wang | Effects of non-rainfall water inputs on ecosystem functions[END_REF][START_REF] Jacobs | Dew deposition and drying in a desert system: a simple simulation model[END_REF][START_REF] Ucles | Role of dewfall in the water balance of a semiarid coastal steppe ecosystem[END_REF][START_REF] Charnes | Measuring the efficiency of decision making units[END_REF] . The annual dew amounts account for 9% to 23% of rainfall in arid regions [START_REF] Ucles | Role of dewfall in the water balance of a semiarid coastal steppe ecosystem[END_REF][START_REF] Malek | Dew contribution to the annual water balances in semi-arid desert valleys[END_REF] . Dew could be used as an alternative source of water during dry season of tropical islands [START_REF] Muselli | Physical, Chemical and Biological Characteristics of Dew and Rainwater during the Dry Season of Tropical Islands[END_REF] . Dew is beneficial to the survival, growth and development of the plants in arid regions or during droughts, such as bringing nocturnal moisture [START_REF] Dou | Near-Surface Warming Reduces Dew Frequency in China[END_REF][START_REF] Trosseille | Radiative cooling for dew condensation[END_REF][START_REF] Steinberger | The Influence of Autumn Dewfall on Spatial and Temporal Distribution of Nematodes in the Desert Ecosystem[END_REF] and being directly absorbed and utilized by leaves through plant stomata or special physical features (e.g., aerial plants) [START_REF] Gerlein-Safdi | Dew deposition suppresses transpiration and carbon uptake in leaves[END_REF][START_REF] Berry | Foliar water uptake: Processes, pathways, and integration into plant water budgets[END_REF][START_REF] Monteith | Principles of environmental physics Second edition[END_REF] . Thus, dew could increase net photosynthate accumulation in leaves [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] and improve plant water use efficiency [START_REF] Monteith | Principles of environmental physics Second edition[END_REF][START_REF] Ben-Asher | Dew is a major factor affecting vegetation water use efficiency rather than a source of water in the eastern Mediterranean area[END_REF] . Dew is also involved in the chemical processes in the atmosphere, such as the diurnal (and nocturnal) cycle of nitrites oxides 3,[START_REF] Tomaszkiewicz | Dew as a sustainable non-conventional water resource: a critical review[END_REF] . Dew frequency decreased by 5.2 days per decade from 1961 to 2010 in China due primarily to near-surface warming and associated decreases in relative humidity (RH) [START_REF] Dou | Near-Surface Warming Reduces Dew Frequency in China[END_REF] . Furthermore, the decreasing rate of dew frequency in arid regions (50%) is higher than that in semi-humid and humid regions in China (40% and 28%) [START_REF] Dou | Near-Surface Warming Reduces Dew Frequency in China[END_REF] . Therefore, dew in different regions have different trends with the global climate change, and dew characteristics under different climate regions are needed to better predict future changes in dew dynamics.

δ 2 H and δ 18 O are natural and traditional hydrological tracers, and play an important role to trace different hydrometeorological processes associated with different types of waters (e.g., rainfall, snowfall, dew, fog, surface water, plant water, and ice core) [START_REF] Dansgaard | Stable isotopes in precipitation[END_REF][START_REF] Jouzel | Deuterium and oxygen 18 in precipitation: Modeling of the isotopic effects during snow formation[END_REF][START_REF] Wang | Partitioning evapotranspiration across gradients of woody plant cover: Assessment of a stable isotope technique[END_REF][START_REF] Lanning | Canopy isotopic investigation reveals different water uptake dynamics of maples and oaks[END_REF][START_REF] Putman | A global database of the stable isotopic ratios of meteoric and terrestrial waters[END_REF] . Two types of mass-dependent fractionation process, equilibrium fractionation and kinetic fractionation, is the fundamental cause of isotope differences during water phase change [START_REF] Zhao | Factors controlling spatial and seasonal distributions of precipitation δ 18 O in China[END_REF][START_REF] Cui | Quantifying the controls on evapotranspiration partitioning in the highest alpine meadow ecosystems[END_REF][START_REF] Soderberg | Stable isotopes of water vapor in the vadose zone: A review of measurement and modeling techniques[END_REF][START_REF] Crawford | Is the isotopic composition of event based precipitation driven by moisture source or synoptic scale weather in the Sydney Basin, Australia?[END_REF] . They are mainly determined by the saturation vapor pressure and the diffusion rate of different isotopes, respectively [START_REF] Griffis | Tracing the flow of carbon dioxide and water vapor between the biosphere and atmosphere: A review of optical isotope techniques and their application[END_REF][START_REF] Winkler | Interannual variation of water isotopologues at Vostok indicates a contribution from stratospheric water vapor[END_REF][START_REF] Jouzel | Water isotopes as tools to document oceanic sources of precipitation[END_REF] . [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess ( 17 O-excess = ln (δ 17 O + 1) -0.528 x ln (δ [START_REF] Ben-Asher | Dew is a major factor affecting vegetation water use efficiency rather than a source of water in the eastern Mediterranean area[END_REF] O + 1)), a newer hydrological tracer, became available to provide additional constraints about moisture transport, rainout, and evaporation to probe hydrological and meteorological processes [START_REF] Barkan | Diffusivity fractionations of H 2 16 O/H 2 17 O and H 2 16 O/H 2 18 O in air and their implications for isotope hydrology[END_REF][START_REF] Aron | Triple oxygen isotopes in the water cycle[END_REF] . Compared with the conventional isotopes depending on both temperature and RH, the [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess is mainly sensitive to the RH between 10°C to 45°C [START_REF] Barkan | High precision measurements of 17 O/ 16 O and 18 O/ 16 O ratios in H 2 O. Rapid[END_REF][START_REF] Cao | Equilibrium mass-dependent fractionation relationships for triple oxygen isotopes[END_REF] . The RH dependence is also confirmed by field experimental observations such as monsoon precipitation and leaf water in Africa [START_REF] Landais | Combined measurements of 17 O-excess and d-excess in African monsoon precipitation: Implications for evaluating convective parameterizations[END_REF][START_REF] Li | Triple oxygen isotope composition of leaf waters in Mpala, central Kenya[END_REF] , precipitation on a subtropical island [START_REF] Uechi | Dominant influence of the humidity in the moisture source region on the 17 O-excess in precipitation on a subtropical island[END_REF] , surface water across the Pacific Northwest, USA [START_REF] Bershaw | Deuterium excess and 17 O-excess variability in meteoric water across the Pacific Northwest[END_REF] , and ice cores in coastal East Antarctica [START_REF] Winkler | Deglaciation records of 17 O-excess in East Antarctica: reliable reconstruction of oceanic normalized relative humidity from coastal sites[END_REF] . The relationship between δ′ [START_REF] Ben-Asher | Dew is a major factor affecting vegetation water use efficiency rather than a source of water in the eastern Mediterranean area[END_REF] O and δ′ 17 O (i.e., the slope of 1000 x ln (δ [START_REF] Ben-Asher | Dew is a major factor affecting vegetation water use efficiency rather than a source of water in the eastern Mediterranean area[END_REF] O + 1) and 1000 x ln (δ 17 O + 1)) [START_REF] Meijer | The use of electrolysis for accurate δ 17 O and δ 18 O isotope measurements in water[END_REF] can be used to better reveal tap water and precipitation formation mechanisms [START_REF] Tian | Spatial and temporal variations of tap water 17 O-excess in China[END_REF] , differentiate drought types (e.g., synoptic drought vs. local drought) [START_REF] Kaseke | Precipitation origins and key drivers of precipitation isotope ( 18 O, 2 H, and 17 O) compositions over Windhoek[END_REF] , and distinguish different types of condensation (e.g., fog vs. dew) in the Namib Desert [START_REF] Kaseke | Nonrainfall water origins and formation mechanisms[END_REF] . Furthermore, it is an effective method to infer the different water evaporation processes experiencing equilibrium fractionation or kinetic fractionation using the relationships between [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess and δ′ 18 O (or d-excess) (e.g., laboratory model test, precipitation, and natural water bodies (river, channels, wells, springs, groundwater, lake and ponds)) [START_REF] Barkan | Diffusivity fractionations of H 2 16 O/H 2 17 O and H 2 16 O/H 2 18 O in air and their implications for isotope hydrology[END_REF][START_REF] Barkan | High precision measurements of 17 O/ 16 O and 18 O/ 16 O ratios in H 2 O. Rapid[END_REF][START_REF] Landais | Combined measurements of 17 O-excess and d-excess in African monsoon precipitation: Implications for evaluating convective parameterizations[END_REF][START_REF] Criss | Principles of stable isotope distribution[END_REF][START_REF] Tian | Stable isotope compositions (δ 2 H, δ 18 O and δ 17 O) of rainfall and snowfall in the central United States[END_REF][START_REF] Li | Continental scale variation in 17 O-excess of meteoric waters in the United States[END_REF][START_REF] Surma | Triple oxygen isotope signatures in evaporated water bodies from the Sistan Oasis, Iran[END_REF][START_REF] Surma | The evolution of 17 O-excess in surface water of the arid environment during recharge and evaporation[END_REF][START_REF] Passey | Triple oxygen isotope signatures of evaporation in lake waters and carbonates: A case study from the western United States[END_REF] .

To the best of our knowledge, there is no daily dew isotope dataset including [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess publicly available. Here, we provide daily dew isotope dataset (δ 2 H, δ [START_REF] Ben-Asher | Dew is a major factor affecting vegetation water use efficiency rather than a source of water in the eastern Mediterranean area[END_REF] (LGR), Mountain View, CA, USA) based on Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) technique [START_REF] Tian | Stable isotope compositions (δ 2 H, δ 18 O and δ 17 O) of rainfall and snowfall in the central United States[END_REF][START_REF] Tian | Stable isotope variations of daily precipitation from 2014-2018 in the central United States[END_REF] , as well as the detailed description of [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess quality control method. The dew isotopic variations have been published in Tian et al. [START_REF] Tian | Investigating the role of evaporation in dew formation under different climates using 17 O-excess[END_REF] . This first publicly available daily dew isotope dataset were presented to fill the gap in global non-rainfall water isotope datasets, especially for 17 O-excess. This would provide data support for scientists to study the global dew distribution characteristics and formation mechanism under climate change. [START_REF] Tian | Investigating the role of evaporation in dew formation under different climates using 17 O-excess[END_REF] . In short, the mean annual temperature (hereafter MAT), mean annual relative humidity (hereafter MARH), and mean annual precipitation amount (hereafter MAP) in Gobabeb is 21.1°C, 50%, and < 20 mm, respectively. The MAT, MARH, and MAP in Nice is 16.0°C, 78%, and 733 mm, respectively. The MAT, MARH, and MAP in Indianapolis is 10.2°C, 69%, and 953 mm, respectively. For Gobabeb, there are concurrent fog and dew collectors installed at the same location, collected water samples were considered dew when water samples appear in dew collector and no samples in the fog collector. For Nice and Indianapolis, fog is rarely seen, dew samples were separated from fog or light rain based on local meteorological information and visual observation of the collecting personnel. All of daily dew samples were collected before dawn to minimize evaporation effects O-excess with small order of magnitude, need to quality control using raw δ 17 O and δ 18 O values to obtain accurate value. The detailed quality-control steps could be found in our previous studies [START_REF] Tian | Stable isotope compositions (δ 2 H, δ 18 O and δ 17 O) of rainfall and snowfall in the central United States[END_REF][START_REF] Tian | Stable isotope variations of daily precipitation from 2014-2018 in the central United States[END_REF] . To summarize, each individual data point was checked using two types of quality control filters. Firstly, because regression coefficient λ (λ = ln (δ 17 O + 1)/ln (δ 18 O + 1)) is the same as mass-dependent fractionation coefficient (θ) during liquid-vapor equilibrium and in water vapor diffusion in air [START_REF] Barkan | High precision measurements of 17 O/ 16 O and 18 O/ 16 O ratios in H 2 O. Rapid[END_REF][START_REF] Angert | Kinetic 17 O effects in the hydrologic cycle: Indirect evidence and implications[END_REF] , and theoretically the θ was found to be 0.511 ± 0.005 for kinetic transport effects [START_REF] Angert | Kinetic 17 O effects in the hydrologic cycle: Indirect evidence and implications[END_REF] and 0.529 ± 0.001 for equilibrium effects [START_REF] Barkan | High precision measurements of 17 O/ 16 O and 18 O/ 16 O ratios in H 2 O. Rapid[END_REF] . Individual data points with regression coefficient λ outside the range of 0.506 and 0.530 were removed. Secondly, [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess values that exceed the range of -100 to + 100 per meg were removed, which exceed the range of observed global precipitation [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess values [START_REF] Barkan | High precision measurements of 17 O/ 16 O and 18 O/ 16 O ratios in H 2 O. Rapid[END_REF][START_REF] Landais | Combined measurements of 17 O-excess and d-excess in African monsoon precipitation: Implications for evaluating convective parameterizations[END_REF][START_REF] Li | Continental scale variation in 17 O-excess of meteoric waters in the United States[END_REF][START_REF] Angert | Kinetic 17 O effects in the hydrologic cycle: Indirect evidence and implications[END_REF][START_REF] Luz | Variations of 17 O/ 16 O and 18 O/ 16 O in meteoric waters[END_REF] . The final [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess value of each dew sample was the mean value of all the individual data points meet the above the two conditions.

Methods

Sample collections.

Data Records

Daily dew isotope database is archived in PANGAEA in a single table including 114 rows and 13 columns [START_REF] Tian | Stable isotope composition of dew measured from 2014 to 2018 in Namibia, France, and the United States[END_REF] . Each row presents a daily dew event at one site. Each column corresponds to the geographic location information (including latitude, longitude, and elevation), isotope variables including three measured individual stable isotopes (δ 2 H, δ 18 O, and δ 17 O) and two calculated second-order isotopic variables (d-excess and 17 O-excess), and three meteorological information including temperature, RH, and VPD. A summary of the dew from July 2014 to April 2018 under three different climatic regions (Gobabeb, Nice, and Indianapolis) is presented in Table 1. The database spanned a large gap over 67.29° in latitude (from 23.55°S to 43.74°N) and 101.3° in longitude (from 86.27°W to 15.04°E). However, the difference in elevation was relatively small ranging from 258 m to 405 m. The meteorological factors show significant difference ranging from 1.39 °C to 21.36 °C for temperature, from 35.3% to 99.8% for RH, and from 0.2 hPa to 52.7 hPa for VPD (Table 1). Figure 1 depicts the distribution of daily dew stable isotopes in the three sites, which was modified from our previous study [START_REF] Tian | Investigating the role of evaporation in dew formation under different climates using 17 O-excess[END_REF] . As for the dew in Gobabeb, the The dew line in Gobabeb between δ 18 O and δ 2 H was δ 2 H = 4.90 ( ± 0.52) x δ 18 O + 1.91 ( ± 1.50) (R 2 = 0.82, p < 0.001), which had the lowest slope and intercept than those in Nice and Indianapolis (Fig. 2a). The slope and Fig. 2 The relationships between δ 18 O and δ 2 H (a), as well as between δ′ 18 O and δ′ 17 O based on daily dew at Gobabeb, Nice, and Indianapolis. intercept in Nice were 6.63 and 8.47, and those in Indianapolis were 6.22 and 1.08. All of the three dew lines were far from the Global Meteoric Water Line (GMWL, δ 2 H = 8 x δ 18 O + 10). The dew line in Gobabeb between δ′ [START_REF] Ben-Asher | Dew is a major factor affecting vegetation water use efficiency rather than a source of water in the eastern Mediterranean area[END_REF] O and δ′ [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O was δ′ 17 O = 0.5202 ( ± 0.0007) x δ′ 18 O -0.0019 ( ± 0.0020) (R 2 = 1, p < 0.001), far from the GMWL for oxygen (δ′ 17 O = 0.528 x δ′ 18 O + 0.035, normalized to the VSMOW-SLAP scale [START_REF] Li | Continental scale variation in 17 O-excess of meteoric waters in the United States[END_REF][START_REF] Luz | Variations of 17 O/ 16 O and 18 O/ 16 O in meteoric waters[END_REF] (Fig. 2b). The slope and intercept of the dew line between δ′ 18 O and δ′ 17 O in Nice (0.5268 and 0.0250) was similar with those in Indianapolis (0.5271 and 0.0286), both of which were close to the GMWL.

Technical Validation

The precisions of different isotopic variables for two international standards (SLAP and Greenland Ice Sheet Precipitation) and the five working standards (LGR#1 to LGR#5) have been measured in our previous studies, which was <0.80‰, <0.06‰, <0.03‰, and <12 per meg for δ 2 H, δ 18 O, δ 17 O, and [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess, respectively [START_REF] Tian | Spatial and temporal variations of tap water 17 O-excess in China[END_REF][START_REF] Tian | Stable isotope variations of daily precipitation from 2014-2018 in the central United States[END_REF] . The precisions of our OA-ICOS measurements are within the range of analyzers using the same technique (0.07‰, 0.05‰, and 10 to 18 per meg for δ 18 O, δ 17 O, and [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess, respectively) [START_REF] Berman | Measurement of δ 18 O, δ 17 O and 17 O-excess in water by Off-Axis Integrated Cavity Output Spectroscopy and Isotope Ratio Mass Spectrometry[END_REF] . Comparing with the Cavity Ring Down Spectroscopy (CRDS) technique, our precisions are comparable with previous studies (<0.98‰, <0.10‰, <0.10‰, and <10 per meg for δ 2 H, δ 18 O, δ 17 O, and [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess, respectively) [START_REF] Steig | Calibrated high-precision 17 O-excess measurements using cavity ring-down spectroscopy with laser-current-tuned cavity resonance[END_REF][START_REF] Affolter | Triple isotope (δD, δ 17 O, δ 18 O) study on precipitation, drip water and speleothem fluid inclusions for a Western Central European cave (NW Switzerland)[END_REF] . Our precisions are also acceptable compared with the traditional Isotope Ratio Mass Spectrometry (IRMS) technique (<0.7‰, <0.3‰, <0.05‰, and <16 per meg for δ 2 H, δ 18 O, δ 17 O, and [START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF] O-excess, respectively) [START_REF] Li | Continental scale variation in 17 O-excess of meteoric waters in the United States[END_REF][START_REF] Schoenemann | Measurement of SLAP2 and GISP δ 17 O and proposed VSMOW-SLAP normalization for δ 17 O and 17 O-excess[END_REF][START_REF] Luz | Variations of 17 O/ 16 O and 18 O/ 16 O in meteoric waters[END_REF][START_REF] Pang | Spatial distribution of 17 O-excess in surface snow along a traverse from Zhongshan station to Dome A, East Antarctica[END_REF] .

  O, δ 17 O, d-excess, and 17 O-excess) under three different climatic regions including Gobabeb-Namib Research Institute (hereafter Gobabeb) in the central Namib Desert with desert climate, Nice in France with Mediterranean climate, and Indianapolis in the central United States with humid continental climate collected between July 2014 to April 2018. Our previous studies have described the operating procedures of Triple Water Vapor Isotope Analyzer (T-WVIA-45-EP; Los Gatos Research Inc.

Fig. 1

 1 Fig. 1 Dew stable isotope variations at Gobabeb (purple colors), Nice (blue colors), and Indianapolis (red colors). From top to bottom: 17 O-excess, d-excess, δ 17 O, δ 18 O, and δ 2 H modified from Tian et al.52.

  δ 2 H values varied from -33.21‰ to 18.17‰ with a mean value of -5.11 ± 14.03‰. The δ 18 O values varied from -6.77‰ to 3.23‰ with a mean value of -1.43 ± 2.59‰. The δ 17 O values varied from -3.55‰ to 1.66‰ with a mean value of -0.75 ± 1.35‰. The d-excess values varied from -19.9‰ to 26.5‰ with a mean value of 6.3 ± 10.0‰. The 17 O-excess values varied from -39 to 45 per meg with a mean value of 9 ± 22 per meg. As for the dew in Nice, the δ 2 H values varied from -114.77‰ to -1.90‰ with a mean value of -37.92 ± 25.91‰. The δ 18 O values varied from -16.65‰ to -0.70‰ with a mean value of -7.00 ± 3.75‰. The δ 17 O values varied from -8.79‰ to -0.36‰ with a mean value of -3.67 ± 1.99‰. The d-excess values varied from 0.1‰ to 32.3‰ with a mean value of 18.1 ± 8.8‰. The 17 O-excess values varied from 7 to 54 per meg with a mean value of 34 ± 12 per meg. As for the dew in Indianapolis, the δ 2 H values varied from -83.99‰ to -1.34‰ with a mean value of -39.38 ± 19.81‰. The δ 18 O values varied from -13.39‰ to 0.46‰ with a mean value of -6.51 ± 3.10‰. The δ 17 O values varied from -7.06‰ to 0.24‰ with a mean value of -3.41 ± 1.64‰. The d-excess values varied from -5.0‰ to 32.1‰ with a mean value of 12.7 ± 7.2‰. The 17 O-excess values varied from -5 to 64 per meg with a mean value of 35 ± 11 per meg. Linear least-squares fitting was utilized to determine the slope and intercept of the dew line in the three sites. The numbers in the parenthesis were standard errors of the estimates.

  The daily dew samples were collected in three different climatic regions including 22 samples in Gobabeb (23.55° S, 15.04° E; 405 m above sea level) with desert climate from July 2014 to June 2017, 23 samples in Nice (43.74° N, 7.27° E; 310 m above sea level) with Mediterranean climate from December 2017 to April 2018, and 69 samples in Indianapolis (39.88° N, 86.27° W; 258 m above sea level) with humid continental climate from January 2017 to October 2017. The detail site meteorological information has been descripted by Tian et al.

Table 1 .

 1 Summary of the daily dew record at Gobabeb (from July 2014 to June 2017), Nice (from December 2017 to April 2018), and Indianapolis (from January 2017 to October 2017).

														Vapor
	Site (Country)	Latitude (°)	Longitude ( o )	Elevation (m, a.s.l)	Köppen climate classification		δ 2 H (‰) δ 18 O (‰) δ 17 O (‰)	d-excess (‰)	17 O-excess (per meg)	Temperature ( o C)	Relative Humidity (%)	Pressure Deficit (hPa)
						Minimum -33.21	-6.77	-3.55	-19.9	-39	3.53	35.3	1.3
	Gobabeb (Namiba)	-23.55	15.04	405	Desert climate	Maximum 18.17 Mean -5.11	3.23 -1.43	1.66 -0.75	26.5 6.3	45 9	16.85 11.83	98.3 77.6	52.7 17.1
						Standard deviation	14.03	2.59	1.35	10.0	22	3.67	17.8	14.2
						Minimum -114.77 -16.65	-8.79	0.1	7	3.55	55.1	4.6
	Nice (France) 43.74	7.27	310	Mediterranean climate	Maximum -1.90 Mean -37.92	-0.70 -7.00	-0.36 -3.67	32.3 18.1	54 34	15.25 9.06	94.2 80.4	31.0 14.0
						Standard deviation	25.91	3.75	1.99	8.8	12	3.01	9.6	6.6
						Minimum -83.99	-13.39	-7.06	-5.0	-5	1.39	65.9	0.2
	Indianapolis				Humid	Maximum -1.34	0.46	0.24	32.1	64	21.36	99.8	27.1
	(United State)	39.88	-86.27	258	continental climate	Mean Standard deviation	-39.38 19.81	-6.51 3.10	-3.41 1.64	12.7 7.2	35 11	13.91 4.39	91.9 6.5	6.2 5.0

on isotopes, and they were stored in 15 ml sealed glass vials in Gobabeb and Indianapolis or polyethylene bottles for the dew samples in Nice. All of the 114 dew samples were delivered to the IUPUI (Indiana University-Purdue University Indianapolis) Ecohydrology Lab to measure δ 2 H, δ 18 O and δ 17 O. Here, the detailed daily dew isotopic variations were reported especially for

[START_REF] Zhuang | Relationship between dew presence and Bassia dasyphylla plant growth[END_REF]

O-excess values.

Isotope measurements and 17 O-excess data processing. The

  The known δ 17 O from LGR#1 to LGR#5 is -10.30‰, -8.56‰, -7.06‰, -4.17‰, and -1.39‰, respectively. Additionally, to reduce differences between laboratories, all of the isotope ratios were normalized using Vienna Standard Mean Ocean Water (VSMOW) and Standard Light Antarctic Precipitation (SLAP) once a day[START_REF] Steig | Calibrated high-precision 17 O-excess measurements using cavity ring-down spectroscopy with laser-current-tuned cavity resonance[END_REF][START_REF] Schoenemann | Measurement of SLAP2 and GISP δ 17 O and proposed VSMOW-SLAP normalization for δ 17 O and 17 O-excess[END_REF] . The δ 2 H, δ 18 O, and δ 17 O of SLAP are -427.5‰, -55.5‰, and -29.6986‰, respectively[START_REF] Schoenemann | Measurement of SLAP2 and GISP δ 17 O and proposed VSMOW-SLAP normalization for δ 17 O and 17 O-excess[END_REF][START_REF]IAEA: Reference Sheet for VSMOW2 and SLAP2 international measurement standards[END_REF] . Furthermore,

	17

isotopic variations was measured using a Triple Water Vapor Isotope Analyzer (T-WVIA-45-EP, Los Gatos Research Inc. (LGR), Mountain View, CA, USA; preheated to 50°C) coupled to a Water Vapor Isotope Standard Source (WVISS, LGR, Mountain View, CA, USA; preheated to 80°C)

[START_REF] Tian | Water vapor δ 2 H, δ 18 O and δ 17 O measurements using an off-axis integrated cavity output spectrometer-sensitivity to water vapor concentration, delta value and averaging-time[END_REF]

. The detailed operation has been described by Tian et al.

[START_REF] Tian | Spatial and temporal variations of tap water 17 O-excess in China[END_REF][START_REF] Tian | Stable isotope variations of daily precipitation from 2014-2018 in the central United States[END_REF]

. To ensure the accuracy of the T-WVIA performance, LGR#1 to LGR#5 as five working standards from LGR were analyzed after every five samples. The known δ 2 H from LGR#1 to LGR#5 is -154.0‰, -123.7‰, -97.3‰, -51.6‰, and -9.2‰, respectively. The known δ 18 O from LGR#1 to LGR#5 is -19.49‰, -16.24‰, -13.39‰, -7.94‰, and -2.69‰, respectively.

Scientific Data | (2022) 9:50 | https://doi.org/10.1038/s41597-022-01151-6

Acknowledgements

Funding for this work was made available from the Indiana University-Purdue University Indianapolis Research Support Funds Grant, U.S. National Science Foundation (EAR-1554894), and the National Science Foundation of China (42007155).

Code availability

No custom code was used to generate or process the data.

Author contributions

C.T. analyzed the samples and wrote the manuscript. K.D. drew the figures and commented on the manuscript. L.W. conceived the idea, collected the samples and edited the manuscript. X.Z., F.L., and W.J. commented on the manuscript. D.B, K.K., and M.M. collected the samples.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information

The online version contains supplementary material available at https://doi. org/10.1038/s41597-022-01151-6.

Correspondence and requests for materials should be addressed to L.W.

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