dataesr/scientific-paragraphs-categorization huggingface dataset:
State and inter-state multi-stakeholder platforms. The geodatabase includes five platforms: • The Rio Grande Compact Commission -U. S. inter-state platform -monitors water flows and administers water-sharing among the states of Colorado, New Mexico, and Texas. We mapped its domain through three spatial layers: one representing the spatial scope of the Interstate Compacts (including the Rio Grande Compact), one identifying the gauges used to monitor and account for states' water delivery obligations to each other, and one identifying the "post-Compact" reservoirs used to manage states' water delivery obligations. Table  1. List of geospatial layers for the Hydrology category including their description, format and input data sources. • The San Juan Chama Project (SJCP) -U. S. inter-state platform -moves water through a trans-mountain diversion from the Colorado River basin in the state of Colorado (San Juan River) to municipal and irrigation users in the upper and middle Rio Grande valley of New Mexico.
We mapped this project with three vector datasets: one for the trans-mountain diversion, one for the reservoirs where imported water is stored and transferred, and one displaying the users of that water (counties, municipalities, irrigation districts, tribe). • The Rio Grande Project -binational and inter-state platform -supplies water for irrigation from the Elephant Butte Reservoir to several U. S. and Mexican irrigation districts. Its domain is represented through two vector datasets: one representing the dams used for storage (Elephant Butte, Caballo) plus the diversion dams for distributing water in the U. S. and Mexico (Percha, Leasburg, Mesilla, American, Riverside -which is inactive, International); and one representing the irrigation districts supplied by the Rio Grande Project (EBID, EPWID#1 and DR 009 Valle de Juarez).
• The Consejo de Cuenca del Río Bravo (Rio Bravo Watershed Council) -Mexican inter-state platform -is a multi-state entity that develops and implements a basin management plan for the CONAGUA Hydrological-Administrative region VI in Mexico. We mapped its domain using its administrative boundary. • The Rio Grande Basin Roundtable in southern Colorado -intra-state platform -regroups various stakeholders such as counties, municipalities, and conservancy districts to identify consensual strategies that meet competing needs in the region, and to provide support and funding for basin projects  29. The geodatabase maps the boundary of its domain of action. Groundwater-focused institutions. This dataset maps the local entities and related infrastructures associated with the administration, management, monitoring and conservation of groundwater in the U.
S. institutions to jointly assess priority shared aquifers along the U. S. -Mexico border  30. Two of the four transboundary priority aquifers underlay the RGB: Mesilla/Conejos-Médanos and Hueco Bolson. Irrigation distribution organizations. This sub-category maps the boundaries of the organizations that divert and coordinate surface water delivery to irrigators, and ensure maintenance of the irrigation conveyance systems, in both the U. S. and Mexico. Our dataset provides the name and the area managed by the organizations, in addition to other specific information for each state and country. We sought to gather the spatial boundaries of three organizations: the irrigation districts (Distritos de Riego in Mexico), ditch companies, and community ditch associations, an umbrella term under which we include the acequia systems of Colorado and New Mexico, and Unidades de Riego (Irrigation Units) across the states of Mexico.
However, spatial datasets of ditch companies and community ditch associations were unavailable for New Mexico and Mexico. Land management. This dataset maps four types of land management: Certified Ejido & Communal Land, Public, Native American/Tribal, and Private/Not Reported. The dataset results from the compilation of two national datasets: the National Surface Management Agency Area Polygons released by the Bureau of Land Management (BLM) that maps state and federally owned lands as well as Native American tribal lands in the U. S. , and the Perimetrales de los núcleos agrarios certificados produced by the Registro Agrario Nacional (RAN) that maps ejido and comunidad (communal) lands in Mexico. When compiling the datasets, we labelled all areas that were neither classified by the BLM nor the RAN as "Private/Not reported".
Protected areas. This dataset maps the areas designated as protected by the International Union for Conservation of Nature (IUCN). Border control. This sub-category maps three key components of border control along the international reach of the Rio Grande/Río Bravo: the U. S. Southwest Border Patrol Sectors associated with the migration apprehension statistics; the border crossing ports of entry; and vehicle and pedestrian fences/barriers. U. S. border control is very active and has important implications for the management of riverbank and riparian areas. However, this research to-date has not covered policies or practices by Mexican border-related agencies that affect water management or flow.
Soil and water conservation districts (included only for the U. S. ). The vector dataset provided in this sub-category map the outlines of the Soil and Water Conservation Districts (SCWDs) in Colorado, New Mexico, and Texas. SCWDs foster voluntary conservation practices among private and public landowners, helping to manage and protect soil, water, forests, and wildlife at the local level. The SWCDs are a specific kind of district, with a mandate that is particular to the legal and institutional history of land and water management in the U. S. Our research to-date has not found anything equivalent to this formation in Mexico, and even less so, any spatial datasets for it. Collaborative conservation projects. This sub-category includes conservation projects where members of multiple sectors and/or institutions actively work in collaboration to identify, protect, manage, monitor, and/or develop policy for the conservation of specific sites.
Our research to-date has not extended to documentation of all such projects in either the U. S. or Mexico, and did not uncover spatial data sets that document their location. Our datasets map three collaborative conservation projects. Two of them, the Landscape Conservation Cooperatives (LCCs) and the North America Bird Conservation Joint Ventures (BJVs), are transboundary. For both, we mapped the boundaries of the four LCCs and the five BJVs that span the RGB. While the U. S. Fish and  Wildlife Service recently discontinued its funding for the LCCs network, we decided to include these cooperatives in our dataset since the LCCs generated highly relevant information for the RGB in the past. The third project refers to the Instream Flow Program in Colorado. For this project, we mapped the streams and lakes affected by the Instream Flow Program.
Watershed boundary. The geodatabase provides the outlines of the Rio Grande/Río Bravo basin and of its sub-basins. The watershed boundary dataset is derived from the World Wildlife Fund's (WWF) HydroSHEDS product, created from NASA's Shuttle Radar Topographic Mission (SRTM) 15-second Digital Elevation Model (DEM) and released by the Food and Agriculture Organization (FAO) geoportal. River networks. This dataset of vectorized river reaches, derived from the WWF HydroSHEDS products, was also accessed from the FAO geoportal. Aquifer boundaries. We mapped the aquifer boundaries by assembling two national geospatial datasets compiled by the National System of Water Information of CONAGUA and the USGS. Gauging stations. The geodatabase provides datasets for the location of discharge monitoring stations operated by USGS (above Elephant Butte Dam), IBWC/CILA (below Elephant Butte Dam and along the border to the Gulf of Mexico), and CONAGUA (tributaries of the Rio Grande/Río Bravo coming from Mexico).
Water Use & Hydraulic Infrastructures (USE). Water Use & Hydraulic Infrastructures datasets provide information related to consumption and diversion of water. Withdrawals. This dataset provides estimates in million cubic meters per year of water used at the U. S. county and MX municipio level. For both countries, estimates are available by type of source (surface or groundwater) and by sector of use (e. g. , public supply, domestic, industrial, irrigation, thermoelectricity). For the U. S. , the data set also breaks down estimates by fresh water and saline water. Irrigation. We provide two datasets (one per country), which reports on a county/municipio basis the irrigated area estimated in hectares and the spatial coverage of different systems of irrigation (surface, sprinkler, micro-irrigation and other), estimated in hectares for U.
S. and in number of farms for Mexico. For the U. S. , we used the original classes (surface, sprinkler, micro-irrigation) and for Mexico, we clustered as follows: lined and earthen canals in surface; sprinkler and micro-sprinkler in sprinkler; and drip in micro-irrigation. Water rights/permits. This sub-category displays the allocation of surface water rights and groundwater rights at the state level for U. S. and Mexico. A water right is a property right that conveys the right to use a certain amount of water. These are administered by the Offices of the State Engineer in the U. S. and by CONAGUA in Mexico. The datasets report the geographic coordinates of the water right holder, and when available, the amount of water right authorized (in acre feet in the U. S. and in cubic meters in Mexico), the name of the water right holder, the adjudication date, the rank of priority (i.
e. , the water right's seniority in a water drainage), or the type of use. Dams. We provide two vector datasets. The first one, derived from the National Inventory of Dams, maps all dams in the U. S. exceeding a certain height and capacity of storage. The second one maps all dams recorded by the CENAPRED in Mexico. Water diversion structures. The geodatabase provides three vector datasets of water diversion structures (ditch/ canal, pipeline, underground conduit and connector). The first one covers the five MX states and is derived from a Mexican national data source. The second one covers the three U. S. states and is derived from a U. S. national data source. The third one covers Colorado with more details and is derived from a state data source. Wells (included only for the U. S. ). We compiled four datasets of well locations.
Socio-Economics (SE). This category includes five datasets related to demography, economics, and transportation networks. Population. This vector dataset reports the total population for the years 1990, 2000, and 2010 on a county/municipio basis. We assembled national demographic census information distributed by the U. S. Census Bureau and INEGI. Population density. This raster dataset depicts population density (number of persons per square kilometre) at 30 arc-second resolution (approximately 1 km at the Equator) for the years 2000, 2005, 2010, 2015, and 2020. Income. Due to the lack of consistent data for both countries, the spatial database includes two heterogeneous datasets: the personal income for the U. S. counties from 1969 to 2015 and the income distribution (i. e. , the distribution of the working population per category of income) in 1990, 2000, and 2010 for the Mexican municipios.
Biophysical environment (BIo). The biophysical environment category provides ecological, pedological, topographical, and land-use/land-cover datasets. Ecoregions. Ecoregions are areas with similar patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. Ecoregions can be mapped for different nested levels. For North America (Canada, U. S. and Mexico), data are available for three levels (Level I being the coarsest and Level III the finest). Ecoregions level IV have also been delineated for the U. S. but not yet for Mexico. Each of those datasets are included in the geodatabase, although ecoregion Level IV is missing for Mexico. Habitat. We compiled six national datasets due to a lack of transboundary coverage.
In the U. S. , we mapped wetlands and deepwater habitats (extent, approximate location, and type), riparian areas, and critical habitat for endangered and threatened species. In Mexico, we mapped Ramsar wetlands sites (representative, rare or unique wetlands that are important to conserve biological diversity), priority sites for restoration (areas of high biological value that require restoration actions), and priority focus sites for biodiversity conservation (conserved habitats that host endangered and threatened species and are adjacent to the protected areas). Soil cover. This vector dataset displays different soil units based on the FAO taxonomy at 1:5,000,000 scale. Elevation. This raster data, derived from the Harmonized World Soil Database (version 1. 1), provides the median elevation aggregated to 30 arc-second grid cells.
It is derived from the NASA Shuttle Radar Topographic Mission Digital Elevation Model and the USGS GTOPO30. Slope. We calculated the median slope in percent and in degree at a 30 arc-second resolution from the elevation data described above, by using the Spatial Analyst tool Slope in ArcGIS. Land cover. This raster dataset, derived from the North American Land Change Monitoring System (NALCMS), depicts the land cover in the RGB, based on 2010 Landsat satellite imagery. NALCMS uses the level II of the Land Cover Classification System (LCCS) standard developed by FAO. Fig. 3  of the Rio Grande/Río Bravo basin depicting land ownership, protected areas, and major rivers, generated from the geospatial datasets of the geodatabase. Land use. Due to a lack of consistent transboundary datasets, we gathered national datasets to map land-use in the RGB.
For the U. S. , we used the yearly USDA Cropland Data Layer, a high-resolution crop-specific land-use image, available from 2008 to 2018 at a 30-meter resolution. For Mexico, we derived our datasets from two governmental datasets. The first one is a spatial dataset of land uses for 2011 and 2014 at a 30-meter resolution, with a coarse classification of crops (rain-fed vs irrigated crops). The second one, non-spatial, is derived from the 2007 Census and provides on a municipio basis planted area estimates (in hectares) of annual spring and winter crops and perennial crops. Data gaps. Although we developed an extensive database, it is not comprehensive for either the U. S. or Mexico. Our approach led us to identify several spatial data gaps for socio-environmental research in the RGB. Yearly crop-specific datasets for Mexico.
Boundaries of community ditch and acequia associations (in NM) and Unidades de Riego (MX). To the authors' knowledge, these spatial boundaries are not publicly available. Mapping them is important, because of their water management implications. The NMOSE is currently working on updating and assembling this information for New Mexico 31. Land ownership. For Mexico, we did not find any spatial datasets delineating public lands, except the Certified Ejidos and Communal Lands. Generating a dataset that differentiates, in more detail, public from private lands in Mexico would be valuable for land and water management research.
Groundwater wells (MX). We were not able to access a spatial dataset of wells for Mexico. Mapping well locations is of primary interest for surface and groundwater management in the basin. Binational infrastructures. Some information on binational infrastructure is missing, such as the Morillo drain (described in Supplementary Information 1). Climate records are also critical for understanding hydrological processes in the basin. While not included in our geodatabase, Livneh and others  11  published a transboundary historic dataset that derives observed daily precipitation, minimum and maximum temperature gridded to a 1/16° (~6 km) resolution for the time period 1950-2013.
technical Validation To ensure the reliability of the spatial database, we draw on a critical assessment of three factors: the quality of the source of information, transboundary discontinuities, and geoprocessing. Regarding the sources of information, we sought to download most of the raw datasets from official sources such as intergovernmental organizations and governmental agencies (e. g. , FAO, USIBWC, USGS, USDA, CONAGUA, INEGI, CONABIO, CONANP, CODWR, NMOSE, TCEQ) or from joint projects overseen by reputable universities, research institutes, and non-governmental organizations (e. g. , from the NASA Socioeconomic Data and Applications Center, the North American Environmental Atlas). We compiled a few datasets (around 6%) from private entities (associations, districts) as they were the only source of the desired information.
For example, this is the case for the border fences. We downloaded the spatial data from Reveal from The Center for Investigative Reporting and OpenStreetMap contributors (https://github. com/cirlabs/border_fence_map) who traced the fences along the border by using open-source mapping tools and by digitizing detailed PDF maps obtained through a Freedom of Information Act request  32. The creators of the data set acknowledge slight differences with the official government agency numbers. However, this is the most detailed border fence map publicly available. We also gathered four datasets -that were not downloadable online -by email requests from three organizations. The RGWCD provided the boundaries of the RGWCD and the Groundwater Management Subdistricts #1, #2, #3, #4, #5, #6 in Colorado.
The CSCB (Colorado State Conservation Board) and the TSSWCB (Texas State Soil and Water Conservation Board) shared the boundaries of the SWCDs of Colorado and Texas, respectively. Although these datasets were not gathered from governmental sources, we believe that these datasets are the best available and we rely on the accuracy of data collection of the three organizations. Whenever possible, we used global or bi-national input datasets to avoid inconsistencies related to differences in resolution, type of information, or geoprocessing methods among counties and states. Around 11. 5% of our raw datasets have a global or bi-national spatial extent, and therefore cross international and state boundaries. Nevertheless, a significant amount of the raw datasets we gathered were nation-wide (70%) or state-wide (18.
5%), which could lead to some of the inconsistencies previously mentioned. All information gathered from Mexican sources was translated from Spanish to English and verified by a bilingual speaker. Regarding the geoprocessing operations, for all datasets, we used several tools and functions available on ArcGIS, such as Project, Clip, Dissolve, Slope, Join, Merge and we rely on the accuracy of this software to create the final outputs. A series of quality checks has been carried out to catch as many potential coding errors as possible. Attributes were checked by using visual inspection. For quality control, transparency, and reproducibility, processing steps are thoroughly detailed in the metadata of each dataset.
Usage Notes This spatial database offers a wide range of applications for transboundary and interdisciplinary research. We provide three examples drawing from our own research. 1. A broad range of mapping products can be created for the whole RGB or sub-sections, such as a map of the main irrigation districts and dams in the RGB (Fig. 2 ), a map of land managers and protected areas (Fig. 3 ), and a comparative map of water use and population on a county/municipio basis (Fig. 4 ). 2. The geodatabase can support spatially explicit and integrated modelling for the RGB  27. We are currently developing a simulation model for the RGB that draws on water and land governance datasets (political jurisdiction boundaries, land management and protected areas, irrigation district outlines), as well as hydrologic and biophysical environment datasets (river network, elevation, water bodies, gauges, land-use), socio-economic (population), and water use and hydraulic infrastructures (dams).
3. The geodatabase can be used to conduct spatiotemporal and geostatistical analyses. Examples of interdisciplinary topics include: (i) evaluating whether water use correlates with environmental variables (ecoregions, land use), socio-economic factors (population), or governance (e. g. , related to land ownership, irrigation district, or state); and (ii) assessing land-use change at multiple scales (local, state, national). For example, we are currently analysing the spatial patterns of land fallowing in the basin. Users of the compiled datasets should cite this data paper following the recommended citation format of the journal. Furthermore, we encourage users to refer to the sections "Credits" and "Use limitations" in the metadata of each layer file, as additional data constraints and required credits of the original data sources may apply.
In conclusion, to achieve more sustainable transboundary water management, it is necessary to approach problems from a boundary-spanning and interdisciplinary perspective. By creating a basin-wide, socio-environmental geodatabase for the RGB, we address this issue and support further interdisciplinary research in the basin. The main novelty of our approach was to include geospatial datasets mapping public and private water governance, which contributes to better understanding of scale interactions and mismatches between ecological and social (decision-making) boundaries  33. Moreover, we were able to identify data gaps, especially for the Mexican side of the RGB, and therefore the need for further research to reduce data imbalance between the two sides of this transboundary basin. Even though our study is limited to the socio-environmental boundaries of the RGB system and the typology of actors is specific to the shared basins between U.
acknowledgements The project was funded by Grant No. G15AP00132  from the  United States Geological Survey. Its contents are solely the responsibility of the authors and do not necessarily represent the views of the  South Central Climate Adaptation Science Center  or the  USGS. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for Governmental purposes. We are grateful to all the institutions that helped us to find, shared, and allowed us to reproduce the input datasets. We thank the  Digital Scholarships Lab at the University of Oklahoma Libraries  ( Sarah Pugachev ,  Tara Carlisle  and  Jeff Widener ) for advice on the database development and publication. We would also like to thank the anonymous reviewers for their thoughtful comments that helped us to improve our manuscript.
In schizophrenia, altered transcription in brain and peripheral tissues may be due to altered expression of the microRNA biogenesis machinery genes. In this study, we explore the expression of these genes both at the cerebral and peripheral levels. We used shinyGEO application to analyze gene expression from ten Gene Expression Omnibus datasets, in order to perform differential expression analyses for eight genes encoding the microRNA biogenesis machinery. First, we compared expression of the candidate genes between control subjects and individuals with schizophrenia in postmortem cerebral samples from seven different brain regions. Then, we compared the expression of the candidate genes between control subjects and individuals with schizophrenia in three peripheral tissues. In brain and peripheral tissues of individuals with schizophrenia, we report distinct altered expression patterns of the microRNA biogenesis machinery genes.