dataesr/scientific-paragraphs-categorization huggingface dataset:
We tested success rate of these tools against hand-checked validation data sets and characterized quality and quantity. A post-processing toolkit was developed to standardize and harmonize data sets, and to integrate this improved content into VertNet for broadest reuse. The result of this work was to add more than 1. 5 million harmonized measurements on vertebrate body mass and length directly to specimen records. Rates of false positives and negatives for extracted data were extremely low. We also created new tools for filtering, querying, and assembling this research-ready vertebrate trait content for view and download. Our work has yielded a novel database and platform for.
Introduction A trait constitutes any measurable or observable morphological, structural, behavioral, physiological, or phenological characteristic of an organism. Traits are expressed as phenotypes, with respect to which key aspects of organismal fitness and ecological roles in communities are defined. Comprehensive taxonomic and geographic views of trait diversity enable ecological and evolutionary characterizations of populations, species and communities, and constitute a powerful tool to understand their temporal  (1) , spatial  (2) , and community dynamics  (3). Given their central importance in biology, trait data have been captured and reported broadly for centuries, but have only recently begun to be re-assembled into global trait databases  (4) (5) (6). Despite these efforts, digitally accessible trait data remain grossly incomplete and non-representative taxonomically, geographically, environmentally, temporally and functionally.
Concerted efforts to close gaps, even when considering the most impressive initiatives to assemble and aggregate global scale data sets, such as the TRY plant function database (www. try-db. org, 7), are far from being complete. These efforts present spatial undersampling of at least an order of magnitude for most areas, especially in the tropics  (8). The predominant digitally accessible resources for vertebrate traits are a repackaging of information from primary or secondary literature  (6, 9, 10) , where trait mean values or ranges are associated with species names. These trait-by-species compilations, while valuable, provide few ways to assess quality (e. g. they lack pointers to the original observations, sample sizes and variances) and largely ignore variation among individuals. However, this missing information could provide fundamental insight into biological mechanisms at multiple spatial and temporal scales  (11).
Relating trait variation to underlying genetic or ecological variation is critical, e. g. to understand the genetic basis of adaptive change or the impact of climatic or habitat change  (12). Maintaining trait data associations to individual organisms offers a number of advantages, because it allows: (i) aggregation of traits and production of distributions of trait variation within species, including new modeling techniques for trait evolution that utilize this information  (13) ; (ii) detection of outlying trait values that may inform further discoveries and investigations; (iii) association of intraspecific trait variation to geography, ecology, and time which is particularly important for understanding trait variation change; (iv) revision of trait variation within and among species as taxon concepts are revised; and (v) cross-validation of species-level trait data derived from the literature or trait databases found in community resources, such as AmphibiaWeb (http://amphibia web.
g. color). Finally, we counted how many different descriptors and synonyms were found in each of these broad categories. In order to gather trait content likely to be common and of broad value to ecologists and evolutionary biologists, we focused our extraction of trait data explicitly on body mass and body length, along with information on sex and life stage. Body mass and length have been shown to be essential integrator variables widely used in ecological and evolutionary studies  (26) (27) (28)  to represent a relatively simple starting point for trait assembly using informatics tools. Sex and life stage are attributes of individuals that are of critical importance for proper interpretation of all other measurements, and so were deemed essential to include in the extraction. To assure that we used standard definitions for traits, we annotated the extracted data using the Vertebrate Trait Ontology (VTO)  (29)  for definitions of body length (http:// purl.
Extracting trait data The next step was the extraction of the values for the target traits (body length, body mass, sex and life stage) from the 'dynamicProperties', 'occurrenceRemarks' and 'fieldNotes' fields. Extracting the target content was particularly challenging because there is no consistently followed standard to guide how data are recorded on labels or digitized. For example, there are multiple types of body length measurements, some specific to a particular vertebrate subclade, and a variety of synonyms and abbreviations for these distinct measurement types. Extraction relied on a multi-step process (Figure  1 ). First, we determined the categories of traits for which we would extract values. In the broader category of vt:body length, we established a set of subcategories that were found in the data: total length, head-'body length', 'snoutvent length', 'standard length' and 'fork length'.
These subcategories reflect distinct community standards and measurement practices. For example, 'snout-vent length' is a term used explicitly for reptiles and amphibians  (30). Similarly, 'standard length' and 'fork length' are measurements specific to fishes (31; Florida Museum of Natural History, Ichthyology Collection, http://www. flmnh. ufl. edu/fish/discover/fish/anatomy/features-measurements, accessed 16 September 2016). Vt:body mass, dwc:sex and dwc:lifeStage did not require subcategorizing. After completing this step of defining the categories of traits and auxillary attributes to extract, we refined the original synonym list generated as described earlier, assembling a list of distinct terms that represent 'body length' (https://github. com/rafelafrance/traiter/blob/v0. 2/lib/trait_parsers/total_ length_parser.
py#L253) and 'body mass' (https://github. com/rafelafrance/traiter/blob/v0. 2/lib/trait_parsers/body_ mass_parser. py#L211) for use in the subsequent step. The categories 'sex' and 'life stage' had more limited numbers of variations, and these were incorporated directly into patterns to seek, also in the subsequent step. The greatest challenge was to develop code to examine each record's relevant Darwin Core fields and automate the extraction of traits, associated measurements, and units. We developed a regular expression (regex) library, written in Python, to find the substrings corresponding to the trait terms, measurement values, and units related to vt:body length, vt:body mass, dwc:sex and dwc:lifeStage. This regex code is available within a Jupyter notebook on GitHub (32https://github.
com/rafelafrance/traiter/blob/v0. 2/parsers/vertnet_parser. ipynb). We chose to use regex instead of other possible techniques, such as machine learning, because we were concerned that, although the database for training and testing seems large, the variety of synonyms might render too few examples for this alternate approach to be immediately useful. The extraction process required careful ordering of string matching patterns. First, we focused on the most common and explicit strings used to refer to a given trait (e. g. 'total length:'). Then we searched for rarer and more abbreviated forms (e. g. 'TL' or 'ToL' for total length) or pattern matches (e. g. recognizing that the first value of '235-97-31-25 ¼ 71. 9' represents a total length of 235 mm, and the last value a body mass of 71. 9 g).
We provide a detailed description of the regex matching in the Jupyter notebook referenced earlier and refer readers there for full details. To address the challenge of tuning code to limit false positives (FPs) and false negatives (FNs) during extraction we developed unit tests iteratively for all variants of body length and mass descriptions. A set of Python unit test suites was developed using Python's unittest library. Each test suite is a Python class and every test is a method in that class. We ran the tests from the command line using the default test loader and test runner from the unittest library. These test cases were checked (see the section 'Validating the extracted content', below) to assure that intended results were achieved, specifically to avoid new FPs, as new expression extractions were developed and test cases added.
This process is also fully documented in the Jupyter notebook. This type of unit testing, however, could not cover all of the possible permutations found in the full data set and required careful vetting and editing discussed in detail in the section 'Validating the extracted content', below. The final output from the extractions was a quadruplet for each trait of interest: (i) the original Darwin Core field from which the observed trait was extracted, (ii) the standardized trait type, (iii) the value of the trait and (iv) the units, if reported.
Validating the extracted content Regular expressions were developed iteratively, with multiple validation steps to test efficiency and to correct for errors in the output. In each validation step, random sets of 500 records (obtained by adding a column to the full set of records, populating it with random numbers, sorting that column, and taking the first 500 records in the list) were manually checked for accuracy from the postprocessed records. Accuracy rates were determined by counting the FP (i. e. the information that was not in the data as published for a given trait or that clearly did not represent meaningful trait values, but was extracted by our code) and FN (i. e. the information that was given in the published data, but not extracted by our code) results. We made a distinction between these errors and situations in which there is no explicit string to signify what is being represented (e.
Once clades were assigned, we determined the vertebrate species names in each clade by one of the following criteria: (i) concatenation of the dwc:genus and dwc:specificEpithet fields; or, (ii) the first two words in the dwc:scientificName field. For length and body mass traits, we determined the number of species that had extracted trait data in three categories-at least 1 record, >10 records and >100 records. In order to give some perspective on the utility of trait data, we provide an example that demonstrates using aggregated data in outlier detection. Specifically, we have provided an illustration of outlier identification for a particular rodent species (Tamias minimus), using data from VertNet extracted with Traiter (Supplementary Materials S1, Supplemental Figure  S3 ).
Bringing trait data back into the VertNet portal The next step was to repopulate the VertNet portal with the extracted and standardized data so that they can be easily discovered and used. The records with the extracted traits were archived on the CyVerse Data Commons  (25). The extracted trait data have also begun to be included in the VertNet snapshots, which are periodic taxonomic subsets of VertNet records, and are also available on CyVerse  (35) (36) (37) (38) (39). To extract trait information from the original data, we developed a Python-based application ('Traiter' hereafter; https://github. com/rafelafrance/traiter/releases/ tag/v0. 2), which operates on each row of extracted trait information and provides the following output (and data types): hasLength (boolean); hasMass (boolean); hasSex (boolean); hasLifeStage (boolean); lengthInMM (numeric); massInG (numeric); wereLengthUnitsInferred (boolean); wereMassUnitsInferred (boolean); derivedSex (string-the value from dwc:sex if it is not empty, else sex extracted by Traiter); and, derivedLifeStage (string-the value from dwc:lifeStage if it is not empty, else lifeStage extracted by Traiter).
In order to include harmonized trait contents in VertNet, we created a fork of the original Traiter code and used it as the basis for a data processing suite (https:// github. com/VertNet/post-harvest-processor/releases/tag/ v2016-09-16). This suite prepares data harvested from IPTs for indexing, for periodic snapshots, and for the VertNet portal. Specifically, the post-harvest processor creates csv files with fields populated exactly as they will be needed for indexing in the VertNet data store. Each of the new fields is generated using the adopted Traiter code in the postharvest processor and becomes part of those csv files that are added to the index, which in turn makes them directly searchable as part of each VertNet record. Records with length ranges (e. g. 'standard length: 20-35 mm') in the verbatim original are given a value for 'lengthtype' from one of five types of length range (total length range, standard length range, snout-vent length range, head-body length range or fork length range) and a value of True for 'hasLength'.
Range values remain in the field in the record from which they were discovered, but the values are not indexed or searchable, nor are they currently available in their extracted form in the Darwin Core archives from the data publishers. Though a range might signify an uncertainty in the measurements, most range values are from specimen lots with more than one individual per lot, as is common especially for fish, reptile and amphibian collections. Since we do not know the measurements for individual organisms in these cases, and because searching for values falling within ranges can complicate both user interfaces and results received from searches, we decided not to index these types of length values. The VertNet portal and APIS were redesigned to support searches not only using the fields in the original records, but also using the trait values the newly added filters such as 'hasMass'.
Traits extraction and algorithm performance The extraction code achieved an error rate well below 1%. In particular, we found a total of 62 errors (Table  1 : FN þ FP) from a check of 2000 records. 'Sex' accounted for half of the FPs, which, in almost all cases, was due to parsing verbose information about the sex of more than one individual (not the actual specimen). Conversely, 53. 13% of the FNs corresponded to 'life stage' and were due to the data presented only as values without a key (e. g. 'adult' as opposed to 'life stage: adult') and were not recognized by the code. MCCs show that trait extraction performance is excellent (Table  1 ). Extraction utilizing vetted code (i. e. code that was reviewed after testing units were performed, see the 'Extracting trait data' section in 'Materials and Methods') yielded results summarized in Table  2 , where numbers of records were adjusted according to error rates found for each trait (see more on error rates below).
We were able to extract length values from 875 602 records and mass values from 736 891 records. Both body length and body mass were most often captured in the 'dynamicProperties' field (80. 67 and 92. 43%, respectively). The majority of the records that had body length data included units (70. 79%; 29. 21% required units to be inferred) and 81. 16% of the records that had body mass included units. Life stage and sex data were also extracted from the fields 'dynamicProperties, occurrenceRemarks and fieldNotes' and their prevalence was compared against that of the corresponding Darwin Core field (i. e. dwc:lifeStage and dwc:sex) (Table  2 ). In the Figure  2. Categorization and relative prevalence of trait descriptors found in the 'dynamicProperties' field. Bubbles sizes are proportional to the number of distinct descriptors found for each body region and qualitative or measurement category.
Percentages for allTraitDescriptors sum up vertically to 100%, while the overall percentages of qualitative and measurement each sum up horizontally to 100% independent of the allTraitsDescriptors percentages. wholeOrg: terms referring to whole organism characteristics (e. g. total body weight); partOfOrg: terms referring to parts of an organism (e. g. hind foot); orgPartsComplex: terms referring to organism body part complexes (e. g. head þ body); nonOrg: non-organismal information (e. g. nest characteristics). False positive, FP, code extracted information that was not in the original, false negative; FN, code did not extract information that was in the original; TP, true positive; TN, true negative. analysed data set, life stage information was found only in other fields in 22. 76% of the records, while sex data were found >42 times more often in the Darwin Core sex field than in any of the other three fields (1.
A new VertNet trait feature The information extracted using Traiter has been incorporated in the underlying VertNet data store, and the VertNet portal (http://portal. vertnet. org, accessed 16 September 2016) now contains an advanced search mechanism for querying on traits. The advanced search feature, along with results, are shown in Supplementary Figures  S1  and S2  for a search where 'hasMass' has been selected, and where a search has been done for records with 'total mass' between 60 and 70 g. For any record where a trait value was detected, we provide the trait information in the displayed visual output for each record, along with the more typical data contents shown. In all cases, all downloaded data contains the harmonized trait measurements.
Significant amounts of hidden trait data Biodiversity collections, in sum, contain an enormous amount of information on the condition, coloration, size and other fundamental characteristics of organisms  (21, 40). In this work, we focused on extracting body mass and length measurements from vertebrate occurrence records because these traits are often regarded as integrator variables. We also captured sex and life stage information since these strongly enhance utility of all other trait data extracted. Yet, this only scratches the surface of the data still hidden in specimen records. Figure  2  provides evidence that there are far more trait descriptions for beaks and gonads than there are for body characteristics such as mass and length. There is no question that significant and diverse trait data can be further assembled using smart approaches, akin to other text mining methods for biodiversity data (41).
Harmonizing the heterogeneous Although we have demonstrated success in pulling trait data out from VertNet-published records and utilizing tuned regular expressions, our process is not perfect and we acknowledge that improvements and new methods could be employed to further reduce error. Part of the issue is that local data capture process is not homogeneous across data publishers, and the structure of trait data can vary greatly depending on the protocols and tools used, on who performs such capture (experts vs. non experts), and on whether the data have undergone quality controls later in the process. All these factors contribute to the heterogeneity found in trait data, and should be taken into account when developing more powerful tools for trait extraction. We advocate, in particular, moving beyond simple regular expression matching to methods that utilize more of the  syntactic and semantic structure of the content.
This work sets the stage for making these tools and processes part of the VertNet publication and data quality workflows and, in doing so, points the way for other efforts, as digitized specimen records flood into aggregators such as (http://idigbio. org, accessed 16 September 2016; 24) and GBIF (http://gbif. org, accessed 16 September 2016; 49). Ideally, standardization and tools developed here should be broadly integrated early in data publication workflows rather than post-publication, especially given that it reduces challenges with extraction downstream. However, the best way to proceed with trait publication steps cannot be answered by VertNet data managers alone; instead, it is a larger community conversation that can now begin with data publishers to address their interests and needs.
Data accessibility Data are accessible on the VertNet portal (http://portal. vertnet. org, accessed 16 September 2016), assuring that traits remain fully associated with data records. We have made the data set of > 6 million complete records used in this study on the CyVerse Data Commons  (25). We create archives of VertNet, including the trait content, on a periodic basis, and put those on CyVerse, as part of our routine processes, where they are assigned DOIs  (35) (36) (37) (38) (39). These are harvestable for use by other resources such as TraitBank (60; http://eol. org/ info/516) upon publication. The most recent archives include trait content. All code developed as part of this project and referenced throughout the text is publicly available on GitHub in tagged releases that reflect the state of software as used for this study, with the core repository and associated Jupyter notebook available at https://github.
Data that are accessible via VertNet include specimens and observations associated with taxonomic as well as geographical and geological context. Records are published as Darwin Core Archive files (Remsen et al. , 2010) using the Integrated Publishing Toolkit (IPT; Robertson et al. , 2014; http://www. gbif. org/ ipt, accessed 16 September 2016), a free and open-source web application for biodiversity data sharing developed by the Global Biodiversity Information Facility (GBIF). The published archives are discoverable on the Internet, and in many cases, hosted by VertNet on its own IPT instance. September 2016, code: https://github. com/VertNet/web app/releases/tag/v2016-09-16) provides search functionality. Darwin Core Archives are harvested (https://github. com/ VertNet/gulo/releases/tag/v2016-09-16) and indexed (https://github.
Manteia is an integrative database available online at http://manteia. igbmc. fr which provides a large array of OMICs data related to the development of the mouse, chicken, zebrafish and human. The system is designed to use different types of data together in order to perform advanced datamining, test hypotheses or provide candidate genes involved in biological processes or responsible for human diseases. In this new version of the database, Manteia has been enhanced with new expression data originating from microarray and next generation sequencing experiments. In addition, the system includes new statistics tools to analyze lists of genes in order to compare their functions and highlight their specific features. One of the main novelties of this release is the integration of a machine learning tool called Lookalike that we have developed to analyze the different datasets present in the system in order to identify new disease genes.
INTRODUCTION Manteia is a data mining system that includes several OMICs data produced for human, mouse, zebrafish and chicken. These data include functional annotations, biological pathways, protein motifs, gene expression, genetics, interactomics, molecular complexes, phenotypes and human diseases originating from various public databases  (1). Data are processed upstream so they can be compared and used together across species. Manteia offers tools to explore each type of data independently but also to combine them in order to answer complex biological questions and make predictions. This can be done using a specific query language called QueryBuilder designed to address one or sev-eral Boolean questions to the system. This can be achieved as well by combining a mixture of independent tools using a data mining module called Refine.