dataesr/scientific-paragraphs-categorization huggingface dataset:
The user can also choose to display data for species of all growth forms available in the original dataset (herbs, shrubs, trees, and others), or only for a selection of these growth forms. When a selection is made, a new PCA is run using the corresponding subset of the dataset. Boxplots at the top of the page show the ranges of values for the selected traits covered by the selected species (Figure  1 ). By default, principal components 1 and 2 of the PCA run on all six traits for all species are displayed (Figure  1a ) which corresponds to Figure  2a  shown in  Díaz et al. (2016). The correlation circle (showing the correlation between the traits and the principal components) was superimposed to the plot using a scaling factor. Arrows ending close to the dotted circle describe traits that are well represented on the selected plane.
| Plotting data onto the phenotypic space of the global spectrum The second functionality of PhenoSpace ("Project your data" tab) allows the projection of new data from an uploaded file onto the user-defined phenotypic space of the global spectrum. Therefore, any dataset providing values for at least two of the six traits can be easily compared with the worldwide trait distribution. The first step is to prepare a csv file that contains the data to be projected. Trait values should be organized in columns whose headings correspond to the abbreviations of trait names spelt out exactly as shown in Table  1. Trait values should be expressed in the units shown in Table  1 , and no data transformation is required before the upload. Each line of the dataset can describe an individual, a population, a species or even a community ("entity" hereafter).
Entities with missing values (coded NA) are excluded from the analysis. No data completion or gap-filling method is performed in the application. If it is considered useful for a specific dataset, it should be performed by the user before uploading the file. Extracolumns are allowed and can subsequently be used to identify subsets of data on the plot (see below). The following conditions must be verified on the application set- The optional extracolumns in the data file can be used to identify data points according to the attributes of the criteria specified in the column (which can be quantitative or qualitative). Here, we show an example of the projection of 55 plant species representative of five successional stages (from recently abandoned agricultural fields or rangelands to mature forests) in Mediterranean southern France (see  Navas et al.
| Download figures and tables The third tab (Downloads) allows the user to save the figures in a png, pdf, or svg format specifying the image size and resolution (for png files). Color changes and the manipulation of graphic elements can be easily done on vector graphics (svg format) once downloaded. When a dataset is projected onto the PCA, entity coordinates on the principal components can be downloaded in an automatically produced csv file. These coordinates are added to the originally uploaded table as extracolumns. The file also contains a header describing the list of traits and the number of species from the original   et al. , 2010)  on the global spectrum. Specific stem density was missing in the uploaded dataset and was therefore excluded from the analysis. The boxplots in the upper part show the trait distributions in the global spectrum and in each successional stage separately.
See Table  1  for trait abbreviations dataset that has been used to run the PCA (see section 2. 1). These coordinates can be used for further analyses. For example, the coordinates of the 55 plants species studied in  Navas et al. (2010)  on the two first components of the global spectrum were downloaded from PhenoSpace. The five successional stages were then compared using a posthoc analysis on these coordinates (Tukey tests, Figure  3 ). F I G U R E 1 (Continues) We found that early-successional species had lower values than latesuccessional species on the size axis (PC1) while no difference between stages was found on the leaf economics axis (PC2).
| D ISCUSS I ON AND CON CLUS I ON The This can be done not only for species, but also for populations or genotypes within species, or even for higher taxonomic ranks or communities. PhenoSpace is a service to the community, which is in principle similar to, for example, the taxonomic name resolution service (TNRS,  Boyle et al. , 2013)   In these cases, one can consider that the use of large datasets such as the original dataset explored by  Díaz et al. (2016)  is more appropriate to construct a meaningful multivariate space. PhenoSpace relies on this idea as imported data are considered as inactive entities in the PCA. Finally, there is an increasing interest in the description of volume and shapes of phenotypic hypervolumes  (Blonder, 2018). The global spectrum provides a standardized multivariate space for each set of traits, from which hypervolumes of projected data can be computed (using the download functionality, see section 2.
3) and easily compared. PhenoSpace is an open source project (available on a GitHub repository at: https://github. com/jsegr estin/ pheno space). The application will be regularly updated to make sure that it provides the most detailed phenotypic space of plants as our understanding and description of plant phenotype improves. We will therefore work in close collaboration with plant trait databases (e. g. , the TRY database, see  Kattge et al. , 2011 Kattge et al. , , 2020) )  to include new species and/or traits in future versions of the application. Our decision rule will be to include a new trait when data are available for a large amount of species in common with the current global spectrum and displays a wide range of variation. The version number, a report detailing the new features, and links to old versions of the application will be available on the webpage.
reference in plant trait-based research (907 citations to the Díaz et al. , 2016 paper in Google Scholar as of 4 August 2020). In this context, we developed the application PhenoSpace, which fulfills the practical goal of positioning plants in the phenotypic space of the global spectrum, making it possible to compare trait variation at any level of organization against the worldwide background. This serves a major aim of comparative plant ecology, which is to put specific sets of individuals, populations or species into a broader context, facilitating comparison and synthesis of results across different continents and environments using relevant functional axes. More precisely, PhenoSpace allows one to: (a) visualize the outcome of various multivariate analyses run with the original dataset compiled by Díaz et al.
ting before uploading the file: (a) the separator and decimal characters are well specified on the "Project your data" tab; and (b) if the imported file misses any of the six traits from the original dataset, they should be unticked on the "Customize the PCA" tab. The csv file can then be uploaded by clicking on the "Browse button" on the "Project your data" tab. Once the importation is completed, entity trait values from the imported dataset are visualized as (a) boxplots drawn for each trait, displayed next to those showing the distribution of trait values from the original global spectrum dataset (top of the page); and (b) additional data points appearing on the selected plane of the PCA (Figure2). For the latter, the imported data are treated as inactive individuals in the PCA: The projection of each entity on the PCA planes is calculated using the trait loadings on the principal components of the global spectrum (see section 2.
I G U R E 1 Screenshots showing the PhenoSpace page displays ("Customize the PCA" tab) for different selections of variables and species groups. (a) all traits and species from the original dataset analyzed in Díaz et al. (2016); (b) plots for herbaceous species using only four traits: Plant height (H), Leaf area (LA), Leaf mass per area (LMA) and Seed dry mass (SM); (c) distinct density areas for the three main growth forms (for each growth form, the 0. 50 and 0. 95 density areas are represented); (d) positions of four species (1 Arabidopsis thaliana, 2 Datura stramonium, 3 Quercus alba, and 4 Sequoia giganteum) in the plane defined by the first two components of the PCA run on the whole dataset F I G U R E 2 Downloaded figure from PhenoSpace showing the projection of 55 species representative of five successional (from stage 1: recently abandoned agricultural fields or rangelands to stage 5: mature forests) stages in Mediterranean southern France (see Navas.
or the calculator developed by Pierce et al. (2017)  to allocate positions to species/populations in Grime's plant strategy scheme (see below). It provides a web interface that allows one to compute entities coordinates in the global spectrum in a very simple way what could otherwise be done only with much more effort or not at all. Here, we discuss three benefits of using this phenotypic space as a reference, instead of running independent analyses for new datasets. First, principal component analyses are generally preceded by scaling of trait data aiming to compare the variation of several traits, although expressed in different units and covering various ranges of values. This procedure often uses the distribution observed in the dataset as a reference, giving equal weightings to the range of values covered by each trait in the construction of principal components.
However, it is rarely assessed whether the observed distributions cover a significant range of values, as compared to global datasets. The imbalance of trait representativeness can lead to a distortion of the resulting PCA. For example, centering and scaling a dataset that includes low variation in size traits but large variation in leaf traits, regarding global distribution, will result in a PCA, which over-disperses entities on the size axis as compared to the leaf axis. In PhenoSpace, we used the worldwide distribution of trait values to center and scale the imported trait data, resulting in a more accurate description of the multivariate trait variation. Second, a low range of values or a small number of entities can result in an inaccurate estimation of trait covariations.
a reference in plant trait-based research (907 citations to the Díaz et al. , 2016 paper in Google Scholar as of 4 August 2020). In this context, we developed the application PhenoSpace, which fulfills the practical goal of positioning plants in the phenotypic space of the global spectrum, making it possible to compare trait variation at any level of organization against the worldwide background. This serves a major aim of comparative plant ecology, which is to put specific sets of individuals, populations or species into a broader context, facilitating comparison and synthesis of results across different continents and environments using relevant functional axes. More precisely, PhenoSpace allows one to: (a) visualize the outcome of various multivariate analyses run with the original dataset compiled by Díaz et al.
F I G U R E 1 Screenshots showing the PhenoSpace page displays ("Customize the PCA" tab) for different selections of variables and species groups. (a) all traits and species from the original dataset analyzed in Díaz et al. (2016); (b) plots for herbaceous species using only four traits: Plant height (H), Leaf area (LA), Leaf mass per area (LMA) and Seed dry mass (SM); (c) distinct density areas for the three main growth forms (for each growth form, the 0. 50 and 0. 95 density areas are represented); (d) positions of four species (1 Arabidopsis thaliana, 2 Datura stramonium, 3 Quercus alba, and 4 Sequoia giganteum) in the plane defined by the first two components of the PCA run on the whole dataset F I G U R E 2 Downloaded figure from PhenoSpace showing the projection of 55 species representative of five successional (from stage 1: recently abandoned agricultural fields or rangelands to stage 5: mature forests) stages in Mediterranean southern France (see Navas.
Trait Definition Functional significance Abbreviation Unit Range of variation in the Díaz et al. (2016) dataset Adult plant height The shortest distance between Light capture, above-ground H m 0. 001-90 the upper boundary of the competition, dispersal photosynthetic tissues of an adult distance of seeds plant and the ground level Stem specific The ratio of the mass of the stem or Growth potential versus SSD mg/mm 3 0. 06-1. 39 density a unit thereof assessed after drying mortality risk from to its volume assessed without biomechanical or hydraulic drying failure Leaf area The area of a leaf in the one-sided Light interception, energy LA mm 2 0. 79-2. 79 × 10 6 projection balance Leaf mass per The ratio of the dry mass of a leaf Photosynthetic rate, leaf LMA g/m 2 4. 9-1,507 area to its area longevity, seedling relative growth rate Leaf nitrogen The ratio of the quantity of nitrogen Protein content (RubisCo), N mass mg/g 2.
Being able to link clinical outcomes to SARS-CoV-2 virus strains is a critical component of understanding COVID-19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the 'Fast Healthcare Interoperable Resource' (FHIR) implementation guide, we introduce an ontology-based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data-driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID ('Global Initiative on Sharing All Influenza Data'), immediately resulting in a measurable increase in the fraction of cases with useful patient information.
GISAID, established originally as the Global Initiative on Sharing All Influenza Data  (Elbe & Buckland-Merrett, 2017) , has widened its remit with the EpiCoV ™ database to become the principal platform for the sharing of genomic sequences of SARS-CoV-2 (hCoV-19) from around the world. Such convergence by the global scientific community around a single database is critical to permit a near-real-time analysis of how the virus is evolving. While currently only 1 out of 258 confirmed cases  (Worldometers Coronavirus, n. d. )  sees the virus sequence submitted (i. e. 36,080,088 COVID-19 cases and 139,967 published SARS-CoV-2 sequences as of 1 October 2020, which indicates that circa 1 out of 258 cases are sent for virus sequencing), it represents the most thorough surveillance of an emerging virus outbreak in history (Massive coronavirus sequencing efforts urgently need patient data -Nature India, 2020).
Two snapshots were taken to assess the uptake of this feature. One month after the change (  15 May 2020), only 3% provided relevant information for this field, for instance, 9% (506/5122) of submitted isolates have this field filled in and of these only a third (164) have provided clinical information (Figure  1a ). At the 6-month mark (01 October 2020), this increased to 13% of entries with data other than 'unknown'  (15,907/125,654) ; however, the usefulness of this data remains variable (Figure  1b ). The word clouds highlight that 'unknown' remains the largest fraction and that the free-text field gives rise to a wide range of different descriptions identifying the same status. There are hence two areas where current processes hamper sustainable and meaningful data collection. Firstly, information is currently not captured in a standardized form that is tailored to COVID-19 infections; secondly, patient information is frequently not available when genomic information is submitted, and workflows are not set up to amend entries retrospectively.
| C AP TURING CLINI C AL DATA IN S TANDARDIZED FORMS Data that are collected and submitted to a central repository such as GISAID likely come from multiple sources, with consequently a wide range of digital-readiness levels. For example, it might be extracted from Electronic Medical Records (EMRs) where the data are already in a structured form. However, it may also be that relevant information needs to first be extracted out of digital-or paper-based clinical notes. In the latter case, the same clinical symptom might be described differently, complicating downstream reporting or grouping of records. Hence, converting clinical observations into standardized terms, so called clinical terminologies that are applicable across the world, is relevant. Figure  2  illustrates this problem on the concept 'loss of sense of smell', which has several synonyms, such as 'anosmia' and 'absent smell', but is represented as a single concept in the 'SNOMED CT' (Systematized Nomenclature of Medicine Clinical Terms) terminology.
| Emerging standardization for COVID-19 There are multiple efforts that currently aim to define the minimal COVID-19-relevant clinical data. The World Health Organization (WHO) has developed a casebased reporting form and data dictionary, as well as interim guidance to clinicians regarding case definitions and clinical syndromes associated with COVID-19 (Table  1 ). Although the WHO's forms are more likely to be accepted by clinical teams around the world, the resulting forms do not capture clinical symptoms and outcomes in detail, for example, only a field for indicating if the patient was showing symptoms but not which symptoms. Similarly, clinical course and outcomes are captured in little detail. F I G U R E 1 Word cloud of GISAID 'patient status' entries, where word size represents number of entries with this term (log10-transformed and pseudocounts to also visualize low frequency).
| What could interoperability look like for COVID-19 Using existing technology and incorporating the above discussed guidelines for COVID-19 symptoms and severity, we built an example FHIR Implementation Guide (FHIR IG) and implemented it as a FHIR questionnaire (see Table  1 ). This allows the flexible collection of relevant terms for a specific use case and allows them to be expressed as an input form for data collection, for example into GISAID. Unlike the FHIR IG from Logica, which focuses on patient care, patient screening, public health reporting and general research, we designed the questionnaire (fields and values) for the specific use case of linking genomic data with clinical outcomes. The FHIR IG captures the following types of information: • interface is also provided-with an example of an implementation for the form used to collect the information shown in Figure  4D.
The FHIR IG provides the guidance needed to build different approaches to data collection. For example, one approach might be to use data extracted from an Electronic Medical Record (EMR) system or a research Electronic Data Capture (EDC) system like REDCap  (Harris et al. , 2019)  for sharing with an organisation such as GISAID. There are existing tools that can be used to facilitate this transformation (Metke-Jimenez & Hansen, 2019). Alternatively, a specific cloud-based web form can be built to capture data and store it in a cloud-based FHIR repository for later analyses. The value sets developed for the different fields in the clinical entry form can be browsed using a terminology browser. Figure  5  shows the symptom-value set in the CSIRO Shrimp browser, a front end for CSIRO's terminology server Ontoserver  (Metke-Jimenez et al.
| CLINIC AL WORK FLOWS NEED TO RE VIS IT ENTRIE S While GISAID enables updates to submitted entries as more patient data become available, updating a submitted entry with clinical information is currently not a wide-spread practice. This in part is due to privacy restriction having prevented the sharing of patient information  (Dyer, 2020). While the current content of GISAID was carefully designed to preserve privacy, adding linkages to clinical databases may require a re-structure even with de-identification protocols in place  (Bauer et al. , 2020 ; Massive coronavirus sequencing efforts urgently need patient data -Nature India, 2020). For example, in regions with low prevalence, the exact location in combination with height and weight can be identifiable. For such a future addition, a clinical record guardian may be needed to provide access to clinical data via a tier system.
Other likely factors are the time-consuming aspect of a task that does not immediately save lives, compounded by the reference laboratories having to chase up busy clinical teams who may not see the immediate benefit. While compiling patient information will remain a labour-intensive task, at least the design of the input forms can help by not increasing the data-entry burden unduly. Walking the fine line between capturing enough data in a standardized way, but also making entry not so onerous to deter individuals from wanting to submit information in the first place, is an ongoing challenge. For our case-study FHIR IG, we have chosen to make most of the data field simple check boxes, with the possibility of selecting more granular concepts using auto-complete style search powered by the terminology server.
| RECOMMENDATIONS In order to assess and detect a shift in the clinical presentation of COVID-19, de-identified patient data need to be collected in a more systematic way. We hence recommend three elements for the medi- 3. Update clinical workflows to revisit entries and update information. Anticipating the opportunity for retrospective data intake in a more controlled fashion, GISAID has a mechanism to reach out to data submitters to update entries. As a more immediate improvement, GISIAD now provides a filter for serving out cleaned data correcting and consolidating 26,838 entries (see consolidated entries as of 15th May 2020 in Table  S2 ), which is aided by a data curation tool. SSV acknowledges grant funding from the Coalition for Epidemic Preparedness Innovations (CEPI).
The lateral cephalometric radiographs were digitized into anonymous image files and FACAD orthodontic tracing software (ILEXIS AB, Linköping, Sweden) was used for analysis. All radiographs were calibrated using the ruler present on the images. Following calibration of all the radiographs, they were analyzed by a single investigator blinded to the origin of the patients. A customized cephalometric analysis was created using the FACAD software to analyze the lateral cephalograms. A total of 16 vertical skeletal and dental parameters (Table  1 ) were measured and the parameters comprised five angular (Figure  1 ), and eight linear measurements (Figure  2 ), with three derived measurements. a desired sample of 25 individuals per group. The total sample comprised 100 lateral cephalograms from 100 patients and this sample size is similar to previously conducted analogous studies  [1, 14].
The lateral cephalometric radiographs were digitized into anonymous image files and FACAD orthodontic tracing software (ILEXIS AB, Linköping, Sweden) was used for analysis. All radiographs were calibrated using the ruler present on the images. Following calibration of all the radiographs, they were analyzed by a single investigator blinded to the origin of the patients. A customized cephalometric analysis was created using the FA-CAD software to analyze the lateral cephalograms. A total of 16 vertical skeletal and dental parameters (Table  1 ) were measured and the parameters comprised five angular (Figure  1 ), and eight linear measurements (Figure  2 ), with three derived measurements. The statistical analyses were performed using SPSS 16. 0 (SPSS Inc. , Chicago, IL, USA). Cephalometric parameters were compared between the two samples, stratifying  The statistical analyses were performed using SPSS 16.