dataesr/scientific-paragraphs-categorization huggingface dataset:
A comprehensive molecular interaction map for RA The RA map graphically illustrates signalling pathways, gene expression regulation, molecular mechanisms and cellular phenotypes involved in the pathogenesis of the disease. As shown in Figure  1 , and explained in detail in the methodology section, the RA map requires exhaustive literature curation, information mining from relevant databases along with continuous updating and advice from domain experts. Importantly, the interactions shown in the diagram represent a graphical model encoded using a standardized format, making the map computationally tractable. For the construction of the map, we used the graphical editor CellDesigner  (18). In Figure  2 , one can see an overview of the RA map. We constructed the RA map following the SBGN Process Description format  (46).
Transforming RA map into a state of the art knowledge base using MINERVA The RA map is available at ramap. elixir-luxembourg. org in the form of an interactive diagram, using the platform MINERVA (Molecular Interaction NEtwoRks VisuAlization) (Figure  3 ). Clicking on a biomolecule in the map, the user can choose to visualize interacting drugs, chemicals and miRNAs. The RA map interfaces with DrugBank (https:// www. drugbank. ca/), CHEMBL (https://www. ebi. ac. uk/che mbl/), CTD (http://ctdbase. org) and miRTarBase (http://mi rtarbase. mbc. nctu. edu. tw). RA map offers custom visualization and export capabilities via MINERVA plugins  (87). For instance, users can explore the RA map starting from a molecule of interest and easily follow its interactions, even throughout a dense and complex network.
Visualizing various datasets We used publicly available datasets for visualization with the RA map. Our goal was to compare the differentially expressed pathways or map regions in different datasets. For this purpose, we used the datasets from transcriptomic data of synovial tissue  (88). We performed differential expression analysis between Berlin, Leipzig and Jena datasets using osteoarthritis as control and visualized the mapping of 122 molecules to the RA map. Most pathways were highlighted, as molecules that lead to most phenotypes were present. Interestingly, we found enrichment for almost all cellular phenotypes except for apoptosis and angiogen-esis. Molecules leading to six out of eight phenotypes were expressed, while molecules linked to the two mentioned phenotypes were absent (Figure  6 ).
All the components and reactions are annotated using only RA and human-specific studies. The RA map is part of the Disease Maps Project, a large scale community effort to comprehensively represent mechanisms for various diseases  (13, 14)  (http://disease-maps. org/). The community fosters the exchange of good practices and promotes the use of standards for the development of disease maps. The standards of curation and graphical representation, as well as the extensive annotation in both human and machinereadable formats of the RA map, ensure transparency, reproducibility and reusability of its content. In 2010 the first RA map was published by Wu et al. They used exclusively high-throughput RA experiments (mRNA, miRNA) described in 28 studies combined with data available in the KEGG database.
A total of 435 species (263 proteins, 58 genes, 48 RNAs, seven simple molecules, one ion, one antisense RNA, 47 complexes), 265 reactions and 10 phenotypes involved in RA were identified using this approach. We decided to follow a different approach as described in the methodology section, in an attempt to limit false positives, increase confidence by incorporating experts' advice and promote the use of SBGN standards for representation to assure reusability of the map. The new RA map we present here includes information from 353 peer-reviewed publications, and it has a significantly bigger size, as it features 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map can also be used as an interactive knowledge base, using the platform MINERVA and serve as a template for overlaying multiple datasets.
Visualization of experimental data could help highlight aspects of the affected biological process and make differences between experimental conditions more evident. Visualizing the results of differential expression analysis of three datasets of gene expression of RA synovial tissues showed enrichment in all cellular phenotypes but not in apoptosis. This finding is in line with the fact that fibroblasts, which constitute a large percentage of the RA synoviocytes, have an apoptosis-resistant phenotype  (107, 108). We performed functional analysis and gene prioritization using BioInfoMiner  (16). The genes that rank higher in this analysis are associated with many systemic pro-cesses and are considered as hubs in the semantic network. Along with prioritization, a pharmacogenomic analysis is provided since the hubs proposed are considered as putative drug targets.
The results of the analyses using GO and PHO terms revealed known RA players, most of which have been already used as drug targets demonstrating that the RA map comprises well-characterized factors and captures most of the relevant systemic processes implicated in the disease. The RA map serves as a curated knowledge base, but it can also be analysed as a complex network. Topological analysis can reveal underlying structural features of the RA map like unconnected parts of the network, or important hubs (well-connected nodes) which are otherwise hard to perceive in large-scale networks. The topological analysis performed in this study revealed connected and unconnected parts of the network. This result reflects our fragmented knowledge on the one hand, but also the use of stringent criteria for the nodes included in the map: experimentally validated interactions in at least two published studies, use of data of strictly human origin and disease-specific.
Conclusion The RA map is the fruit of interdisciplinary collaborations between clinicians, biologists and bioinformaticians. The aim was to build not only a knowledge repository but a versatile tool that can be used for various purposes. The RA map can offer to experimental biologists and clinicians easy access to all molecular pathways implicated in the disease along with references and annotations, to bioinformaticians a template for disease-specific pathway enrichment of omic datasets and finally, to computational modellers a mechanistic scaffold for the inference of a computational model  (5, 6, 109) , providing an intermediate step between a conceptual and an executable model.
Availability of data and material The RA map is freely accessible at ramap. elixir-luxembourg. org The original CellDesigner XML file of the whole map can be downloaded from MINERVA from the INFO section by clicking on the source file (third tab in the left panel of MINERVA website). Right clicking on the main screen also gives an option to export the visible content in three formats -SBML, CellDesigner SBML and SBGN-ML.
Radiocarbon is a critical constraint on our estimates of the timescales of soil carbon cycling that can aid in identifying mechanisms of carbon stabilization and destabilization and improve the forecast of soil carbon response to management or environmental change. Despite the wealth of soil radiocarbon data that have been reported over the past 75 years, the ability to apply these data to global-scale questions is limited by our capacity to synthesize and compare measurements generated using a variety of methods. Here, we present the International Soil Radiocarbon Database (ISRaD; http://soilradiocarbon. org, last access: 16 December 2019), an open-source archive of soil data that include reported measurements from bulk soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO 2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil profile.
The core of ISRaD is a relational database structured around individual datasets (entries) and organized hierarchically to report soil radiocarbon data, measured at different physical and temporal scales as well as other soil or environmental properties that may also be measured and may assist with interpretation and context. Anyone may contribute their own data to the database by entering it into the ISRaD template and subjecting it to quality assurance protocols. ISRaD can be accessed through (1) a web-based interface, (2) an R package (ISRaD), or (3) direct access to code and data through the GitHub repository, which hosts both code and data. The design of ISRaD allows for participants to become directly involved in the management, design, and application of ISRaD data. The synthesized dataset is available in two forms: the original data as reported by the authors of the datasets and an enhanced dataset that includes ancillary geospatial data calculated within the ISRaD framework.
Additionally, many studies and models focus on only the top 0. 5 m of soil or less, despite deeper soils contributing a significant proportion of SOM storage by way of low carbon concentrations but large deep soil mass  (Rumpel and Kögel-Knabner, 2010). There is an urgent need to synthesize a wide variety of soil data to model the role of soil in the climate system  (Bradford et al. , 2016) , to develop more data-driven estimates of soil health  (Harden et al. , 2017) , to inform policy and land management plans that preserve and enhance soil carbon storage  (Minasny et al. , 2017; Poulton et al. , 2018) , and to extend our detailed understanding of soil developed from observations made at the profile scale to both regional and global extents. Here we describe a new open-source database for the synthesis of soil data with a particular focus on soil radiocarbon data.
g. ,  Trumbore, 1993). These applications have led to an explosion in the number of publications with radiocarbon measurements from soil, increasing from a few dozen papers annually during the 1980s to more than 150 per year in the last decade (based on papers with "soil" and "radiocarbon" as keywords). The database presented here is an attempt to provide an archive for all the previously published data but also a repository for organizing new data as it is published. Two recent soil radiocarbon synthesis efforts demonstrate the utility of these data for improving predictions of SOM dynamics  (He et al. , 2016; Mathieu et al. , 2015). Bulk soil radiocarbon measurements, if not part of repeated time series, provide only an approximation of the time elapsed since carbon in the soil was fixed from the atmosphere.
There have been previous efforts to develop a soil radiocarbon database  (Becker-Heidmann, 1996 , 2010; Trumbore et al. , 2011) , separately or integrated with a general-purpose soil carbon database  (Harden et al. , 2017). However, a challenge remains: to compile and organize soil radiocarbon data that have been collected in many different and complex ways (e. g. , using various fractionation methods or including fluxes as well as organic matter pools). Addressing this challenge will provide new opportunities to leverage existing soil radiocarbon data for critical research, such as developing practical and theoretical insights into the information contained in various fractionation methods and how they relate to one another. This will expand our understanding of controls on soil carbon dynamics and facilitate broader integration of radiocarbon constraints on soil carbon turnover in Earth system models.
For example,  He et al. (2016)  leveraged a synthesis of bulk-soil radiocarbon data to better constrain the age of carbon in five Earth system models, demonstrating that without this added constraint, these models overestimate soil carbon sequestration potential by an average of 40 %. Here, we present a flexible database spanning broad spatial scales and capturing a range of data types, including diverse soil fractionation methods, incubations, fluxes, and interstitial measurements and spanning a range of spatial scales. Our goal is to provide an open-access data resource that will encourage the scientific community to apply the database for a variety of synthesis studies or metaanalyses and also contribute data to the repository.
The International Soil Radiocarbon Database (ISRaD) The International Soil Radiocarbon Database (ISRaD) is designed to be an open-source platform that (1) provides a repository for soil radiocarbon and associated measurements, (  2 ) is able to accommodate data collected from a large variety of soil radiocarbon studies, including the diversity of fractionation techniques applied to soils as well as repeated bulk measurements made over spatial or temporal gradients, and (3) is flexible and adaptable enough to accommodate new variables and data types. Although ISRaD was specifically developed with soil radiocarbon measurements in mind, it is well suited for synthesizing other soil measurements, including stable carbon and nitrogen isotopes. Importantly, we currently focus only on natural abundance isotopic measurements and therefore exclude data from isotopic tracer studies.
Database and dataset structure In its most general form, ISRaD is an implicitly relational database. It consists of a linked hierarchical list of tables that contain soil measurements, i. e. , variables (Fig. 1 ). The fundamental unit of organization in ISRaD is the entry, which corresponds to a unique dataset, i. e. , a dataset with a digital object identifier (DOI), while each subordinate table corresponds to data from that entry with a particular spatial or temporal dimension. Transparency and traceability are fundamental tenants of ISRaD. Accordingly, each entry, whether ingested individually or as a compilation, must have a DOI. For data from published studies, the DOI of the publication is acceptable. Data from unpublished studies must be registered for a DOI through a DOI registration agency (e.
g. , Zenodo, Pangaea, etc. ) prior to ingestion into ISRaD. As it is equally important to be able to reconstruct prior data compilations, e. g. , synthesis studies, the specific references for individual datasets making up a synthesis are ingested as part of the synthesis entry, and the entry is flagged within the database with an additional reference to the synthesis study itself. For example,   in  an entry; the horizontal bars distinguish the hierarchical levels of the database. Arrows show the hierarchical relationship between and among levels of the database. Time is considered at the profile level, as this is the coarsest spatial scale for which observational data are reported. Every time a profile is sampled, a unique profile identifier must be generated, consisting of the profile name combined with the profile observation date, which is then linked to all measurements made at or below the profile level of the hierarchy.
several of the major data sources added to ISRaD were synthesis studies (e. g. ,  He et al. , 2016; Mathieu et al. , 2015) , and users can generate reports of data from these prior syntheses by constructing a query that utilizes this synthesis flag. Each ISRaD release will be available in two forms: (1) a raw version of data (ISRaD_data), containing only values that were reported in the original source of each data entry, and (2) an expanded version of the database (ISRaD_extra). The ISRaD_extra version of the dataset includes additional parameters that have either been calculated or imported based on site coordinates, such as geospatially referenced climate information. Table  2  includes examples of some of the new variables included in ISRaD_extra; a more detailed list of this growing list of variables can be found on the ISRaD website in the ISRaD_extra Information File.
Site data table Site-level data are limited to the geospatial details defining the coarsest scale of the study area(s) included in each entry. We define a site as a spatially defined location that includes one or more soil profiles. By convention, we define a site as having a ≥ 5 km radius, i. e. , samples collected within 5 km of each other should be grouped under the same "site" designation. However, the 5 km radius is a convention only, as the distinction between site and profile may be study-specific, and geospatial data at this resolution are not always available for legacy datasets. Spatial coordinates are required to designate a site, and thus the required fields at the site level are limited to the site name (site_ name*), latitude, and longitude. Every entry must specify a minimum of one site location but can include multiple sites that do not need to be located in close proximity.
For entries that do not report spatial coordinates, the data curator may estimate latitude and longitude based on the description of the study area using any of the widely acceptable mapping software (e. g. , Google Earth, Google Maps, etc. ). The site table does not include fields for reporting site properties. Such directly measured variables are reported at the profile level. The intended purpose of the site-level data is to provide at least coarse-scale geospatial coordinates for extracting consistently sourced parameters from geospatial datasets, which can then be compared against the range of measurements reported at the profile level.
Flux data table Soil flux data present a special case of observations that correspond to the profile level of the database hierarchy. Fluxlevel data allow for reporting temporally explicit measurements of mass or energy transfer occurring at the profile scale. Both gas and liquid analytes (e. g. , CO 2 , CH 4 , dissolved OC, particulate OC, etc. ) may be reported in flux data. In addition to the profile name, records with flux data must also include the observation date (flx_obs_date). Data mea-sured at multiple time points in a single location will have identical profile names but unique temporal data.
Fraction data table Compared with most other soil databases, the fraction data table of ISRaD is unique. The fraction data fields are designed to accommodate and allow for fair comparison of the wide-ranging methodologies utilized to partition soils into discrete fractions. As such, there are more required fields for the fraction level compared with the other hierarchical levels. These required fields include fraction name (frc_name*); the input source (frc_input), which can be the name of another fraction or bulk (unfractionated) soil layer fraction scheme (frc_scheme), which is a controlled set of terms describing the general class of fractionation procedure used; the fracwww. earth-syst-sci-data. net/12/61/2020/ Earth Syst. Sci. Data, 12, 61-76, 2020 tionation agent (frc_agent), which provides additional detail for methods that have multiple options; the upper and lower boundaries (frc_lower and frc_upper), which allow for description of the fractionation thresholds used in the fractionation procedure; and finally the fraction scheme units (frc_scheme_units), which describe the units of reference for the cutoff thresholds.
Radiocarbon data -reporting conventions Radiocarbon measurements of environmental samples have a long history, much of which is reviewed in  Trumbore (2009) , including common units. Radiocarbon data ingested to IS-RaD are required to adhere to some basic reporting conventions. First, measurements of radiocarbon may be reported in units of either fraction modern (FM) or 14 C. Within the ISRaD_extra version of the database, values reported in one or the other accepted units are internally converted and filled across all entries so that either unit may be used for analyses of the full dataset. Other units are not supported at this time -for example, calibrated radiocarbon dates are not accepted, as the calibration curves evolve over time. Such calibrated ages make sense only for certain fractions (e.
g. , macrofossils found in soils) and do not make sense in the context of most soil organic matter, which is an open system for carbon, with inputs that vary in 14 C over time. For datasets where radiocarbon is reported in units other that FM or 14 C (e. g. , percentage of modern carbon or conventional radiocarbon age), it is up to the data curator or original author of the dataset to convert the reported values to one or both of the permitted units. Second, the year of measurement for each radiocarbon value must also be reported so that values may be internally converted between the two accepted units. In addition to these basic requirements, there are several other optional fields pertaining to radiocarbon data. These include the radiocarbon laboratory; the laboratory number, a unique identifier issued by each AMS facility; the analytical error reported for each measurement reported by most laboratories; and the environmental standard deviation of replicate samples (if analyzed).
Once an entry passes QA/QC, it can be submitted for the final two steps of the ingestion process: expert review and final ingestion. Data templates that have passed QA/QC should be submitted via email to ISRaD at info. israd@gmail. com. These templates are then distributed to ISRaD expert reviewers, who inspect template files to ensure proper completion of the more complex aspects of the template, such as classification of soil fractionation methods. If problems are identified with a submitted dataset during the expert review process, reviewers will work with the data curator to ensure that these problems are corrected. Once the expert reviewer signs off on a submitted template, it will be ingested into the database.
The ISRaD website Most simply, users can access ISRaD data and associated resources by way of the website (http://soilradiocarbon. org, last access: 16 December 2019). From the website, users can download pre-compiled versions of the database in a simple file format (. xlsx or. csv), which can be easily ingested into graphical or database software. The website also provides access to the most recent versions of the ISRaD entry template, the template information file, an up-to-date list of the datasets included in the latest version of ISRaD, the QA/QC tool, and a variety of other resources for assisting with filling out data templates and interacting with ISRaD data.
Accessing ISRaD within the R computing environment and the ISRaD-R package ISRaD has been designed for ease of use in the R computing environment (R Foundation for Statistical Computing, Vienna, Austria) in order for users to be able to take advan-tage of the full suite of R capabilities and functionality to manipulate and analyze ISRaD data. Many of the basic functionalities, such as loading current versions of the ISRaD data objects, can be performed in the R environment without installation of the ISRaD-R package. A number of vignettes including R scripts for some commonly used data manipulations or plotting are given on the website. Users who need to locally compile a version of ISRaD (e. g. , using their own templates) or who want access to the full suite of reporting functions can access these features by installing the ISRaD-R package (also called ISRaD, which is available at the Comprehensive R Archive Network -CRAN -repository, http://cran.