dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , data provided by public agencies and industry bodies, data collected from the literature and data collected on purpose for the study such as experimental data or farm survey data). Once completed, the tabulated dataset was verified against each article to identify and correct any extraction error. For salmonids, given the very different nature of datasets between disciplines, molecular epidemiology papers were assessed separately from non-molecular epidemiology papers. Raw data were considered 'available' when they were provided in the manuscript's main text, as supplementary materials or in an online repository. Data available upon request to the authors were not considered as available in this study. Data considered as raw data in this assessment were non-aggregated data, provided at the level to which they were collected (i.
e. , excluding any summary data). For mathematical modelling studies (simulation studies), the model inputs and parameter values were considered as the raw data for the purpose of this assessment, not the model outputs such as simulation results for example. The rationale for identifying 'available' datasets in this first stage was that these data are potentially findable and accessible outside of the research team which produced them. In some cases, all the data required to reproduce the results may have been provided in the manuscript itself (either in the text or as tables in the article). Such data could not be evaluated for FAIRness as they did not form a distinct digital resource from the article itself. Therefore, they were excluded from the next stage of the assessment described in the next paragraph.
The information required to assess FAIRness was then extracted for each digital resource identified as available. Furthermore, information about the online repositories used to deposit datasets by the articles' authors was obtained from the Registry of Research Data Repositories (www. re3data. org). A list of 13 FAIR maturity indicators relevant for our work was compiled based on the indicators proposed by Wilkinson et al. [6, 12]  (Table  4 ). Maturity indicators should be applied to a single digital resource, and therefore the target resource must be defined clearly, as some of the principles apply to both the data and the associated metadata  [1]. Here, the resources we evaluated were the datasets associated with each paper and identified during the first steps of the work. For molecular epidemiology papers, the datasets of interest were those containing genetic data than the sample or isolate data.
While the authors of this framework have used a binary scoring system (pass or fail) in their maturity indicator tests (w3id. org/ AmIFAIR), we felt that some resources presented some maturity regarding a certain indicator while not entirely meeting the requirement. Therefore, resources were assessed with a 3-level scoring system (2, 1 or 0), according to whether they fully met, partially met or did not meet the requirements for each indicator, respectively. After completion of the assessment, the scores were reviewed individually against each digital resource to identify and correct any error. The assessment of articles and datasets described above was conducted by the first author (AM), while a parallel assessment of 10% of the items included in the study was conducted by one of the co-authors (CF).
At the article level, the availability of raw data, data statement and type of data were assessed by CF for 15 randomly selected articles. A minor discordance was noted for 3 of these articles and assigned to the way these items were assessed. The present Methods section was clarified accordingly. At the dataset level, the FAIRness scores were assessed by CF for 8 randomly selected datasets. Different scores were attributed to 8 indicators out of the 104 indicators which were doubly assessed (13 indicators by dataset). Four of these differences were for the indicator I1, and one each for indicators F1. 1, F3, A2 and R1. Based on these differences, the maturity levels for these indicators were re-evaluated and re-defined (Table  4 ) by the two researchers, and all datasets were re-assessed based on these changes.
Whether this relationship would change by considering species diversity cannot be currently ascertained due to limited taxonomic knowledge in many regions  32. The relationship between detritivore diversity and decomposition, when data were grouped according to latitudinal zone, was most evident in tropical areas, less important in temperate areas and unimportant in boreal areas (although the latter were underrepresented in our dataset). Others have demonstrated a positive relationship between detritivore diversity and decomposition in some streams of boreal areas  33  , but our global dataset indicates a relatively weak relationship when compared to other latitudinal zones. Importantly, the stronger relationship between detritivore diversity and decomposition in the tropics suggests that species losses in these areas, where detritivore diversity is already lower than at higher latitudes as shown here and elsewhere  25, 26  , may cause the greatest impact on decomposition.
5-5. 0, 5. 0-10. 0, 10-20, 20-40 and 40-80 mm) that was assigned to each family using available literature  [56] [57] [58] [59] [60] [61] [62] [63]. Data analyses. We examined the influence of detritivore diversity, abundance, biomass, mean body size, latitude and the interactions between detritivore variables and latitude on decomposition, using generalised additive models (GAMs, gam function, 'mgcv' package v. 1. 8. 31  64, 65.
Lepidostomatidae Sericostomatidae Fig. 5  Distribution of detritivore families in our study, which was predominantly Laurasian (blue) or Gondwanan (green); insert indicates origins of those two regions (≈200 Ma). Photographs represent a subset of families (ordered left to right from the most to the least abundant in our study) and asterisks denote families that were globally distributed but more abundant in one of the two areas. A complete list of families is provided in Supplementary Table  1. Photograph credits: L. Boyero, A. Cornejo, R. Figueroa, N. López-Rojo, F. Masese, J. Pérez, J. Rubio-Ríos, J. Vieira and C. M. Yule. 'MuMIn' package v. 1. 43. 17) based on Akaike weights  66. A model selection approach was used to identify which factors and interactions were included in the models with the highest conditional probabilities (i.
3. 1. 151  69 ) where realm, biome or climate were fixed factors and litter mixture type was a random factor, followed by pairwise comparisons using adjusted P-values (glht and mcp functions, 'multcomp' package v. 1. 4. 13  70 ). The variance was allowed to differ among realms and biomes using the VarIdent structure. Normalised residuals of the final model were inspected with plots of residuals vs. each predictor, and no pattern was observed. Variation in assemblage composition was explored with non-metric multidimensional scaling (NMDS, monoMDS function, 'vegan' package v. 2. 5. 6) 71 calculated on Hellinger transformed abundance data and permutational analysis of variance (PERMANOVA) based on a Bray-Curtis dissimilarity matrix. We compared realms, biomes and climates (adonis function, 'vegan' package), followed by pairwise comparisons (pairwise.
Acknowledgements We thank the many students and technicians who helped with research in different regions ( S. Andrade ,  U. Apodaka ,  K. Barragán ,  A. J. Boulton ,  G. Diedericks ,  R. Roßberg ,  J. Rodger ,  M. Sachtleben ,  A. Tapia ,  A. Villarreal ,  V. Villarreal  and others). This study was part of the  DecoDiv  project conducted by the  GLoBE network  (www. globenetwork. es), which is coordinated by L. B. Most research was based on crowdfunding (details on specific funding sources at each region are given in Supplementary Information). Project coordination was funded by  Basque Government funds (Ref. IT951-16 ) to the  Stream Ecology Group (UPV/EHU, Spain ).
et al. , 2002). As of today, 276 CTAs have been identified and compiled in a unique CTA-Database (http://www. cta. lncc. br/) (Almeida et al. , 2009). With their expansion, new classification criteria emerged. First, CTAs were divided into two groups based on their chromosomal localization: X-CTAs, localized on the X-chromosome, and non-X CTAs, localized on autosomal chromosomes. More than half of the CTAs belong to the first group and are organized in multigene families, while non-X CTAs genes are dispersed along the 22 autosomes. Second, although CTAs are testis-restricted expression, some of them are expressed under physiological conditions in few other tissues, mainly in the brain or in ovary. This led to a further classification in three groups: testis-restricted, testis/brain-restricted and testis-selective, depending on their expression pattern  (Hofmann et al.
V. 1. FIRST APPROACH: IDENTIFICATION OF CTAS ACTING ON MITOCHONDRIA To identify CTAs that can potentially act on mitochondria, we designed a four steps selective process based on online databases (Figure  2 ). In Step 1, using the CTA-Database (http://www. cta. lncc. br/) (Almeida et al. , 2009)  we listed the 276 genes that are encoding CTAs. In Step 2, we analyzed the sequence of all refseq genes and transcripts to eliminate pseudogenes and lncRNA (Supplementary Table  1 , highlighted in gray), reducing the list to 243 CTA genes. In Step 3, using the integrated web resource Mitominer  (Smith and Robinson, 2016) , which lists all proteins with mitochondrial functions (Supplementary Table  2 ), we reduced the CTA list to the proteins that were reported to act on mitochondria, by at least one of the four following databases: -Gene Ontology (GO; http://geneontology.
org), listing proteins associated with mitochondrial processes  (Ashburner et al. , 2000 ; "The Gene Ontology Resource," 2019), -MitoCarta, (https://www. broadinstitute. org/files/shared/metabolism/mitocarta/human. mitocarta2. 0. html), a database including 1157 genes encoding mammalian mitochondrial proteins  (Calvo et al. , 2016) , -Integrated Mitochondrial Protein Index (IMPI, http://www. mrc-mbu. cam. ac. uk/impi) providing a collection of 1626 human genes encoding proteins with known (1184) or predicted (442) mitochondrial localization  (Smith and Robinson, 2016) , -Human Protein Atlas (http://www. proteinatlas. org), listing proteins located in mitochondria using immunofluorescence imaging  (Uhlen et al. , 2015). This led to the identification of 10 CTAs: FTHL17, KIAA0100, POTEH, DDX43, MAGEA11, GPAT2, FATE1, COX6B2, SPATA19 and PAGE1 (Table  1 , figure  2 ).
Gene Among the estimated 2000 different proteins that compose the mitochondrial proteome, 13 are encoded and synthetized within the mitochondria, while the remaining are encoded by the nuclear genome, synthesized in the cytoplasm and then imported into mitochondria. The vast majority of these imported proteins contains the MTS, an N-terminal sequence with particular structure and hydrophobicity  (Roise, 1997)  which can be predicted in silico. We used four online prediction softwares: MitoFates  (Fukasawa et al. , 2015) , MitoProt-II  (Claros and Vincens, 1996) , iPSORT  (Bannai et al. , 2002)  and TargetP  (Almagro Armenteros et al. , 2019). Two of these tools, MitoFates and iPSORT, give clearcut results as "YES" if a MTS is predicted, and "NO" if it is absent. The two others, MitoProt-II and TargetP, provide a probability score, for which we selected the thresholds of 0.
Notably, the MitoProt-II and iPSORT softwares led to larger numbers of isoforms with a predicted MTS, and an intersection including 58 different isoforms, encoded by 33 CTA genes. Interestingly, some CTAs, such as CEP55, GAGE12G, RNF17, SPEF2 and TSAG10, encode different isoforms that are all predicted to harbor a MTS. However, other such as, TSPY1, CTNNA2, CTCFL and ODF2, encode multiple isoforms among which only a few are potentially addressed to mitochondria. The last case illustrates the predominant role of mRNA alternate splicing in targeting proteins to different cell compartments.
Among them, only two (SPATA19 and KIAA0100) were also identified in the first approach. Firstly, MitoprotII predicted the presence of a MTS in the two transcripts encoded by SPATA19. This prediction is in adequacy with previous reports using rats  (Doiguchi et al. , 2002b) , mouse and human models  (Doiguchi et al. , 2002a; Matsuoka et al. , 2004)  showing the presence of a N-terminus sequence corresponding to a MTS. Secondly, 5 out of the 7 transcripts encoded by KIAA0100 were predicted to have a MTS using MitoprotII, and 1 out of the 7 transcripts was predicted to have a MTS using iPSORT. Again, this finding is consistent with a previous bioinformatics study, showing that KIAA0100 harbors 4 domains and one of them, located at N-terminus, is a MTS. This domain is already identified in the yeast mitochondrial protein FMP27  (Zhong et al.
Figures' legends Step 3) Among the 243 CTAs, we identified 10 CTAs linked to mitochondrial functions in at least one of the following databases: MitoCarta, GO Annotation, IMPI score (threshold score >0,5) and the Human Protein Atlas. Step 4) Using PubMed abstract mining, we identified 5 CTAs; COX6B2, GPAT2, SPATA19, FATE1 and KIAA0100 involved in mitochondrial functions. (B) Venn diagram distribution of the 10 CTAs identified in step 3, using the MitoCarta, GO Annotation, IMPI score and Human Protein Atlas databases.
Based on rich community-wide datasets sampled along the elevational gradient and during the two seasons (Table  1 ), we set the following aims: (1) To evaluate the role of flower-visiting butterflies and hawkmoths in plant-pollinator networks and understand how elevation and seasonality affect their relative importance in pollination communities. (2) To analyse the elevational and seasonal changes in the structure of the pollination networks, with a specific focus on specialisation. (3) To assess butterfly and hawkmoth preferences to floral traits, as well as to test potential trait-matching between flowers and their visitors. (4) To test the potential relationship between proboscis length and specialisation of butterflies and moths in flower foraging. We hypothesise that butterflies and hawkmoths represent only a small proportion of the flower-visiting communities, although we expect their importance can be higher in lowlands and in the dry season where and when both groups are more abundant and diverse on Mount Cameroon  29, 30.
4c ). Neither proboscis nor forewing lengths had any significant effect on d' specialisation (proboscis length: Spearman, p = 0. 38, ρ = 0. 16; forewing length: Spearman, p = 0. 63, ρ = 0. 086). Lepidopteran preferences to floral traits. Finally, we analysed preferences of individual lepidopteran families to specific floral traits by multivariate RDA ordinations  33. Flower-visiting lepidopterans showed significant preferences towards certain floral traits (Fig. 5 ). With the dataset of all flowering plants, the selected floral traits explained 25% of the variability in the visitation frequency (Fig. 5a ). The focal families formed three relatively distinct groups. Sphingids preferred sugar-rich, larger and deeper flowers of purple colour. Papilionids, lycaenids, and nymphalids preferred orange flowers, whilst hesperiids and pierids did not express any apparent preferences to floral traits.
In most of these cases (except 8 sphingid morphospecies), we were able to identify visiting lepidopteran to genus and to distinguish the morphospecies from all other species or morphospecies in our dataset, despite that we failed to assign them to any species name. For each visiting Lepidoptera, we determined whether they touched the plant's reproductive organs (anthers, stigmata, or both) to distinguish potential pollinators from other visitors. The recorded interactions were used to reconstruct individual plant-lepidopteran networks for each elevation and season (i. e. eight networks). We used visitation frequency (i. e. number of interactions of each species per plant species during 24 h) in each of the eight networks. This controls for differences in total recording time between plant species in the few cases we failed to find enough replicates, or the recordings were shorter because of dying flowers or technical failures.
The collected specimens were mounted and photographed at the Nature Education Centre, Jagiellonian University, Krakow. On these photographs, we measured forewing length and width, body length, and thorax width, lengths of fore-, mid-and hindleg, and proboscis (Supplementary Fig. S2 ) in ImageJ2  67  , with hundredths of mm accuracy. We assessed the lepidopteran trait collinearity by multiple regression, and selected the proboscis (independent of most other traits) and forewing lengths (correlated with the other traits and therefore being a useful proxy for the specimen size) for analyses (Supplementary Table  S8 ). We analysed patterns of the proboscis and forewing lengths in communities of all lepidopterans measured at different elevations by LMM. The average trait values per species were used as response variable (log-transformed, as the data showed a lognormal distribution), elevation as categorical fixed-effect variable, and lepidopteran families as random-effect variable to correct for inter-family variability.
Consequently, we analysed elevational and seasonal (fixed-effect variables) differences in the proboscis or forewing lengths (response variables) in flower-visiting lepidopteran species only. This dataset involved 34 measured lepidopteran species recorded during flower visits (19 hesperiids, 7 papilionids, 8 sphingids). In both analyses, we applied AICc (AIC corrected for small samples  68  ) to select the most plausible models. Due to the high variability in sample sizes, no post-hoc tests were performed. Finally, we tested the correlations between the proboscis and forewing length of lepidopterans and their d' specialisation using Spearman's rank coefficients; after Bonferroni correction for multiple comparisons, p < 0. 0125 were considered as a significant value in these analyses (α).
Acknowledgements We are grateful to  Nestoral T. Fominka ,  Mercy Murkwe ,  Luma Francis Ewome ,  Raissa Dywou Kouede ,  Esembe Jacques Chi ,  Karolína Hrubá ,  Hernani Oliveira ,  Zuzana Sejfová ,  Pavel Kratochvíl , and other field assistants and students for help in the field; to  Ivan Šonský ,  Sailee Sakhalkar ,  Petra Janečková ,  Eliška Chmelová ,  Marek Rybár ,  Jiří Hodeček ,  Jan Raška  and several other people for help with processing the video-recordings; to Karolina and  Ewelina Sroka  for mounting of specimens; to  Jan Wieringa  and the  Naturalis  staff for help with plant identification; to  Eric B. Fokam  and the  Mount Cameroon National Park  staff for their help with permits and logistics; and to  Matthew Sweney  for correcting our English. This study was permitted by the  Republic of Cameroon Ministries for Forestry and Wildlife and for Scientific Research and Innovation.
MATERIAL AND METHODS Data source, study design and study population This study is based on the CONFINS study ( www. confins. org), a prospective online population-based cohort study of adult volunteers in France which was set up in April 2020 during the first COVID-19 national lockdown, established between March and May 2020. The main objective of the CONFINS study was to investigate the impact of the COVID-19 pandemic and the general lockdown on the well-being and mental health of the population. A large communication campaign was deployed in France on social media and press. Several professional (health workers) and university networks relayed recruitment information. The eligibility criteria were to be more than 18 years old and locked down in the French territory until the end of the general lockdown in France (11 May 2020).
We also performed sensitivity analyses among the complete case population to test the robustness of results regarding the imputation process. Our missing data analysis procedures used missing at random assumptions. 5] [36]  We independently analysed 10 copies of the data, each with suitably imputed missing values, in the multivariate linear or logistic regression analyses. We averaged estimates of the variables to give a single mean estimate and adjusted SEs according to Rubin's rules. We imputed only data from covariates using covariate data and completed the imputation process with other data collected in the CONFINS cohort (having a child, housing type before and during the lockdown, self-rated health and self-rated quality of life before the lockdown). We performed additional imputations for the student subsample since paid activity and financial conditions during childhood had missing data.
Acknowledgements The authors are indebted to the participants of the CONFINS cohort for their commitment and cooperation and to the entire i-Share ( Elena Milesi ,  Marie Mougin ,  Edwige Pereira ,  Garance Perret ,  Clothilde Pollet ,  Aude Pouymayou ),  Kappa Santé  ( Christophe Brunat ,  David Clément ,  Clément Leclercq ) and  Kap Code  ( Vanessa Marie-Joseph ,  Juliette Olivier ,  Salma Rzin ) staff for their expert contribution and assistance, especially in this very particular context of pandemic and lockdown. Contributors CT and SS are the principal investigators of the CONFINS cohort. JA and CT developed the concept and the design of the current study. JA performed the data analysis and drafted the manuscript. IM, MM, SK and MT participated actively in the creation of the CONFINS questionnaires and brought epidemiological, psychological and psychiatric expertise to the interpretation of the results.
Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e. g. , because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts break machine-learning-extracted biomarkers, as well as detection and correction strategies.
Introduction: Dataset Shift Breaks Learned Biomarkers Biomarkers are measurements that provide information about a medical condition or physiological state  [1]. For example, the presence of an antibody may indicate an infection; a complex combination of features extracted from a medical image can help assess the evolution of a tumor. Biomarkers are important for diagnosis, prognosis, and treatment or risk assessments. Complex biomedical measures may carry precious medical information, as with histopathological images or genome sequencing of biopsy samples in oncology. Identifying quantitative biomarkers from these requires sophisticated statistical analysis. With large datasets becoming accessible, supervised machine learning provides new promise by optimizing the information extracted to relate to a specific output variable of interest, such as a cancer diagnosis  [2] [3] [4].
These methods, cornerstones of artificial intelligence, are starting to appear in clinical practice: a machine-learning-based radiological tool for breast cancer di-agnosis has recently been approved by the U. S. Food and Drug Administration  [5]. Can such predictive biomarkers, extracted through complex data processing, be safely used in clinical practice, beyond the initial research settings? One risk is the potential mismatch, or "dataset shift," between the distribution of the individuals used to estimate this statistical link and that of the target population that should benefit from the biomarker. In this case, the extracted associations may not apply to the target population  [6]. Computer-aided diagnostics of thoracic diseases from X-ray images has indeed been shown to be unreliable for individuals of a given sex if built from a cohort over-representing the other sex  [7].