dataesr/scientific-paragraphs-categorization huggingface dataset:
The reason why none of these nodes survived multiple testing by FDR can be explained by the specific choice of the hit list used in the analysis. This means that many genes important for this biological system (OGG1 driven human oxidative DNA damage recognition process) were not targeted in the screening, while they can be present in the randomly generated hit lists thus yielding higher centrality scores for these nodes and increasing their p-values. As it shown below, this does not prevent the paths that were found at the selected FDR to pass through some of these nodes. We examined the list of paths that were found at the selected FDR. Figure  2  presents a subnetwork visualized with Cytoscape software  [64]  for paths with centrality score 3. Figure  2  shows that the method identified two cohesin proteins SMC3 and SMC1A that interact with RAD21 protein to form cohesin-RAD21 complex  [65] , known to be enriched at DNA double-strand break sites and facilitating recombinational DNA repair  [66].
We used networks that represent global human interactome with high-confidence experimentally validate interactions in our work. Nodes that are not present in a particular interactome (e. g. tissue specific interactome) can be excluded from the network, based e. g. on transcriptomic data, to create smaller but more specific networks. On the other hand, it is typical to have poorly studied hit genes in hit lists from functional screens that might not be present in the network. Low confidence or predicted interactions for such hit genes might be added to the network in this case, which is especially important for interaction types other than protein-protein interactions. Most of the protein-protein interactions in our global integrated network come from high confidence proteinprotein interactions in HIPPIE database.
HIPPIE contains interactions from widely used BioGRID  [71] , DIP  [72] , HPRD  [73] , IntAct  [74] , MINT  [75]  databases. All interactions in HIPPIE database have a confidence score which is calculated as a weighted sum of several parameters including number of studies in which an interaction was detected and the type of the experimental technique used to detect the interaction. The confidence score allows to filter out low confidence interactions. High confidence interactions from other databases (e. g. String  [76] ) or separate studies might be added to the network to improve network completeness and/or specificity for a particular biological system. We introduced a notion of final implementer. We denoted a final implementer as a molecule that is involved in events responsible for the development of the final realization of the phenotype in the biological process of interest.
Databases The human integrated interactome was constructed from 7 databases: Human Integrated Protein-Protein Interaction rEference (HIPPIE)  [77] , SIGNOR  [78] , Sig-naLink  [79] , TFactS  [80] , KEGG Metabolic Pathways  [81] , TransmiR  [82] , miRTarBase  [83]. All databases contain experimentally validated interactions, except SignaLink database which contains a small number of predicted miRNA-mRNA interactions. We used only high confidence interactions from HIPPIE database. The confidence threshold was chosen according to HIPPIE documentation  [84]. Since all the databases use different types of gene ID, we converted the ids to the HUGO gene nomenclature and used this nomenclature to construct human integrated network. SI-GNOR database contains some interactions that involve phenotypes, protein families and stimuli; however, we used only interactions between proteins, complexes and small molecules.
The search is done using breadth-first algorithm. The direction of the interactions but not the type of the interactions is taken into account during the 3 a Undirected interactions are not included into the integrated network during merging of databases in case directed interactions exist between the same nodes. b Main steps of the MasterPath. Detailed description is presented in the Method section. c Direction of interactions is taken into account when paths are found using breadth-first algorithm. Only the first two paths will be considered between nodes e and f by the method. It should be noted that the arrow represents here only the direction of the interaction but not the effect (e. g. activation or inhibition) search (Fig. 3c ). Then the centrality score is calculated for each node v as the number of the shortest paths {p} from all combinations of hit genes and final implementers that pass through the node v: c(v) = | p ∈ {p}: v ∈ p |.
Availability of data and materials The datasets generated during current study as well as the source code are available at the GitHub page https://github. com/daggoo/masterPath. The datasets analyzed during current study are included in this published article's supplementary information files and are available as a Supplementary File S1 in the reference publication  [30] , https://doi. org/10. 1371/journal. pone. 0071927. s004 and a Supplementary Table  1  in the reference publication  [55] , https://academic. oup. com/nar/advance-article/doi/10. 1093/ nar/gkaa611/5876283#supplementary-data.
the Hydrometeorological Sandbox -École de technologie supérieure (HYSEtS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950-2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates.
In geosciences, an answer to the problems of monitoring networks has been a steady move towards gridded datasets. Gridded datasets can provide information in regions lacking adequate coverage, but are also native to many remotely sensed, computer modeled and hybrid data sources. Gridded datasets do, however, come with potential issues. Their reliability in most cases depends on the quality of the underlying observation network. In addition, while grids are used as a convenient way to represent physical processes at the regional to global scales, environmental management is not performed at the grid scale. For water resources management, the catchment-scale is the relevant geographical entity. Therefore, relevant databases for water resources management need to provide catchment-based hydrometeorological data as well as relevant physiographic variables.
Complexifying the issue, there are now several available data sources for meteorological forcing data  4, 5. Recent work  6, 7  has shown that impact model results are influenced by the choice of forcing data. Providing several relevant forcing datasets should therefore be considered important to better understand how uncertainties propagate in the water resources management chain. Finally, watershed databases should make all efforts to include as many catchments as possible. A first reason is that it's been shown that local studies are difficult to generalize to other areas  8. There are therefore increasingly many efforts towards large-scale multi-catchment studies  [9] [10] [11] [12]. This trend toward large-scale studies, and therefore large-scale databases, should, however, balance breadth and depths  13.
Another reason to move toward large-scale studies is the challenge of a changing climate. Most early water resources climate change impact studies focused on single catchments. We now know that climate variability is region dependent and scale dependent  14, 15. Hydrological climate change impact studies should therefore be conducted at least at the regional scale and on catchments of different sizes to better understand the impact of internal variability and amplification of precipitation extremes  16  as a function of the spatial scale. Many efforts have been put into building relevant large-scale hydrometeorological databases. The Global Runoff Data Center  17  which has been operational since 1988, was a key effort in trying to regroup many national runoff databases. It is, however, a simple repository of streamflow gauges records with limited metadata.
In most countries, agencies in charge of streamflow monitoring are typically different entities than the ones in charge of weather and climate monitoring. It is therefore not surprising that most current catchment-based hydrometeorological databases originate from university and research center efforts. The CAMELS framework for the US  12  which was extended to Great-Britain  18  and Chili  19  , as well as the CANOPEX database in Canada  20  are examples of such efforts. These databases, however, contain a relatively modest number of watersheds (<800) and only use a single weather forcing dataset. A high-resolution global DEM was combined with many global and national streamflow databases to provide the Global Streamflow Indices and Metadata Archive  21, 22. This allowed for the extraction of a significantly larger number of catchments as well as global coverage.
Hydrometric data. The daily hydrometric data were collected independently for the three countries covered in this database, namely Canada, the United States and Mexico. The Canadian hydrometric data were provided through the Environment and Climate Change Canada (ECCC) Water Survey Canada (WSC) National Water Data Archive (HYDAT), available at: https://collaboration. cmc. ec. gc. ca/cmc/hydrometrics/www/. The data are downloadable in a single Microsoft Access table and include station metadata, such as station location, drainage area and flow regime, as well as the actual daily flow data. A filter was applied to select only the stations whose flow regime is natural, i. e. is not regulated by man-made structures over the study period. This was performed to ensure that the hydrometric data and meteorological data were as naturally correlated as possible.
Hydrometric data for the United States were collected from the United States Geological Survey (USGS) National Water Information Service (NWIS) web portal  23  , available at: https://waterdata. usgs. gov/nwis/uv/?referred_mod-ule=sw. Data were batch-downloaded and processed to obtain full time-series for each station. While some metadata is made available such as drainage area, station coordinates and statistics on historical flow, there is no information on the flow regime or on the presence of regulation structures. An alternative method to filter regulated structures was thus devised. To determine which sites were regulated or were affected by regulation, the streamflow dataset was cross-checked against the peak flow statistics database from NWIS. Stations whose peaks were influenced by regulation works at least once in the archive were removed from the HYSETS dataset.
conagua. gob. mx/CONAGUA07/Contenido/Documentos/Portada%20BANDAS. htm. Metadata is available for every station in the database and includes station coordinates, drainage area and information about the presence/absence of regulation structures. The stations included in the HYSETS database are all located on basins exempt from any regulation structures. Finally, a filter was applied to ensure that all stations had at least one year of recorded streamflow data. All hydrometric data were converted into units of cubic meters per second (m 3 /s).
Watershed delineation. The watershed delineation boundaries are a critical component of the dataset as all meteorological data need to be extracted according to those limits for each watershed. For most of the watersheds, the water management agencies provide the official boundaries directly in the form of a shapefile or geodatabase. The Canadian data are available at: http://donnees. ec. gc. ca/data/water/products/ national-hydrometric-network-basin-polygons/?lang=en and the United States boundaries are available at the following website: water. usgs. gov/GIS/metadata/usgswrd/XML/streamgagebasins. xml. The drainage area was made available for most hydrometric gauges by the water management agencies that collate and curate those sources of data. However, a filter was applied to remove all stations that did not have an official drainage area value at the hydrometric gauge, as the value is key in determining if the watershed bounds are acceptable or not.
The drainage areas were validated using the watershed delineation boundaries as described above in the geospatial analysis software QGIS 3. 4. However, in some instances, the water management agencies did not provide watershed boundary files as they had not been produced or made available publicly. In those cases, estimated watershed contours were taken from the Global Streamflow Indices and Metadata (GSIM) project  21, 22  where available. For catchments where GSIM boundaries were kept for the data extraction a flag ("flag_GSIM_boundaries") was set to 1 to inform users that the boundaries are from GSIM and not from the official agencies. The GSIM-derived area is also identified in those cases in the dataset, under the "Drainage_ Area_GSIM_km2" heading. For catchments smaller or equal to 50 km 2 in size according to the official gauge, a bounding box equal to the surface area around the catchment outlet was provided as the contour of the catchment as at those scales catchment delineations are difficult due to the resolution and hydrometric gauge accuracy.
These catchments are represented by the "flag_artificial_boundaries" indicator in the dataset files. Furthermore, weather data and other catchment attributes are coarser than the area of the catchments in most cases. In the HYSETS database, all boundaries are provided in a WGS84-projected ESRI shapefile and include the following properties: Watershed ID (to link to the data in the netCDF files), source of the data, name of the hydrometric station, official ID of the hydrometric station and the flag to identify if the boundaries were derived from GSIM. All drainage areas are in km 2.
Meteorological data. The HYSETS database contains meteorological data from five sources. The following sections detail the methods applied to integrate the data into the database at the catchment scale. All precipitation data are provided in millimetres per day (mm/d), and temperature values are in degrees Celsius (°C). Station data. Three weather station products were used to cover the North-America domain: The Environment and Climate Change Canada (ECCC) weather stations for Canada, available at: https://climate. weather. gc. ca/, the Global Historical Climate Network Daily (GHCND) station database  24, 25  for the United States and Mexico, available at: https://www. ncdc. noaa. gov/ghcnd-data-access and the station-based serially complete dataset for North America (SCDNA)  26  , available at: https://zenodo.
org/record/3735534. The ECCC and GHCND datasets were combined to provide a North American weather station dataset for raw observations with a potential incomplete coverage for the desired 1950-2018 period. The SCDNA was also added to provide another dataset of stations with complete records between the 1979-2018 period. The daily historical ECCC weather data were downloaded from the ECCC web portal for the years 1950-2018. Data include daily precipitation, maximum and minimum temperature for over 8578 stations across Canada, with varying levels of data completeness and record length. While the GHCND database contained Canada's weather stations as well, the ECCC database was more complete and was preferred. Over Mexico, a total of 5249 precipitation and 5071 temperature stations are available in the GHCND dataset.
Similarly, there are 55693 precipitation and 16011 temperature stations across the United States. While these stations undergo some quality control (QC), both data with and without QC were extracted and provided in the HYSETS database. These stations were combined into a 69520-station dataset for precipitation and 29960-station dataset for temperature. The extraction process was performed twice for station data: Once using the quality controlled GHCND database and another using all available data, even the non-quality-controlled data. The SCDNA provides daily precipitation, maximum and minimum temperature for 27280 stations over North America. A strict quality-control was performed on the original station data by the SCDNA authors. Missing data were infilled/reconstructed using information from neighbouring stations as well as three reanalyses products (ERA5, JRA-55 and MERRA-2).
Strategies based on quantile mapping, statistical interpolation and machine learning were used to implement the corrections. Overall, this dataset was shown to provide a better agreement to station observations compared to four gridded products. All three station datasets (ECCC/GHCND with and without QC and SCDNA) were weighted separately using Thiessen polygons for each watershed. The stations contributing to the Thiessen polygon calculation were those located within an artificial boundary defined as the real watershed boundaries extended by a buffer of 1° of latitude and longitude. This step was performed in order to exclude stations that would be too distant to represent the watershed conditions. Due to the highly variable nature of gauge-data quality and station longevity in the case of the ECCC/GHCND combined datasets, Thiessen polygons were computed for each day, using the available data for each day.
Therefore, there are some discontinuities when a station is added or removed, or when a station temporarily has no record for a given period of time. There are also cases where no data at all were available for a given period. In those cases, the meteorological data fields are set to NaN, and the user is encouraged to replace those data using other means (either manual replacement or replacement with one of the gridded products as described below). The 1950-1978 period for the SCDNA dataset was also set to NaN. All three sets of catchment-averaged data are available in the HYSETS database, opening the possibility of performing quantitative assessments of the impacts of using more (but perhaps less reliable) meteorological data in impact studies. Natural resources canada gridded climate data for canada.
The Natural Resources Canada (NRCan) gridded climate data product was made available from Natural Resources Canada's Canadian Forest Service and covers the entirety of Canada up to approximately 84°N latitude  [27] [28] [29]. It includes daily precipitation, maximum and minimum temperature data on a daily scale on a ~10 km spatial grid. Data cover the period 1950-2010 inclusively. Data points falling within the catchment boundaries were averaged to obtain a single time-series of continuous data as the NRCan dataset contains no missing data. When catchments were too small to contain a data point, the closest data point to the catchment centroid was used to populate the time series for that watershed. Information on the NRCan dataset can be found at the following website: https://cfs. nrcan.
gc. ca/projects/3/4. Livneh gridded climate data for continental USA, Mexico and southern Canada. The Livneh database includes interpolated precipitation and temperature data on a regular 0. 0625 × 0. 0625° grid over the continental United States, Mexico and southern Canada  30, 31. The data cover the period 1915-2015, although only the portion 1950-2015 was used in this dataset. It includes daily precipitation, maximum and minimum temperatures for the entire period without any missing data. Some discontinuities are present at the United States/Mexico border as station density and quality differ and influence the interpolation process. The same is also present but at a smaller scale on the United States/Canada border. The catchment-averaging process was the same as for the NRCan dataset. The Livneh data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.
esrl. noaa. gov/psd/. ERA5 and ERA5-Land reanalyses. The ERA5 32 and ERA5-Land  33  reanalyses are hourly products developed by the European Center for Medium-Range Weather Forecasting (ECMWF). The reanalyses provide estimates of a multitude of hydrometeorological and atmospheric variables including precipitation and temperature on regular grids covering the entire surface of the Earth. ERA5 was first implemented with a 0. 25° x 0. 25° spatial resolution and data are available from 1979-2019, although the HYSETS database stops in 2018 to remain consistent with the other data sources. The ERA5-Land reanalysis is a refined version of ERA5 with a spatial resolution of approximately 9 km. It was driven by the ERA5 reanalysis and a mask was applied such that only land masses are modelled in the refined domain.
ERA5-Land covers the period 1981-onwards, and as such the years 1981-2018 are available at present in the HYSETS database. As both the ERA5 and ERA5-Land products are hourly and data are archived without UTC offsets, it was required to shift the data according to the grid point locations. The longitude of the watershed centroid was used to assess to which time zone it belongs. Based on this time zone, the hourly data were shifted from the same number of hours to realign the daily cycle between 00:00 and 24:00. For instance, a station located in the time zone -7 will have the whole time series shifted by 7 hours to match its proper daily cycle. The ERA5 and ERA5-Land data were downloaded from the Copernicus Climate Data Store, available at: https://climate. copernicus. eu/ climate-reanalysis.
Note that the HYSETS dataset contains modified ERA5 and ERA5-Land reanalysis data from the Copernicus Climate Change Service Information and that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains. Once the data processing was complete to bring it to the daily scale, the extraction followed the same process as for the NRCan and Livneh datasets, however, for the reanalysis products, the data are available for all watersheds given that reanalyses cover the entire globe. Snow Water Equivalent (SWE) data. Two snow-water equivalent databases are provided in HYSETS. The first is a 9-year (2010-2018) time series of watershed-averaged daily high-resolution (roughly 1 km) data provided by the Snow Data Assimilation System (SNODAS) analysis  34  available at: https://nsidc.
The second dataset is the ERA5-Land reanalysis product, which covers the period 1981-2018. It was extracted using the same method as for the ERA5-Land precipitation and temperature data and was also averaged at the daily scale using an hourly UTC offset depending on the time zone. One main advantage of the ERA5-Land reanalysis SWE is that it covers the entire globe and thus is available for all catchments, even those above 54°N that are not covered by SNODAS. physiographic data. One of the strengths of the dataset is the inclusion of a multitude of properties to describe and characterize each of the watersheds. The process is similar for all of the properties, but some variables required slightly more complex operations than others. The first set of data was based on geographic and topographic properties and was derived from the EarthEnv-DEM90 digital elevation model  35  available at: https://www.