Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
422
datasets available to search
ShareScore release 0.9.0
Dataset results
422 results for “Authors”
Brexit Results by Local Authority with Selected Socio-Economic Data
<p>Combined dataset derived from various government sources (see below), allowing comparison of Brexit / EU referendum voting patterns by local authority and socio-economic demographic data. Contains data for Great Britain (England, Scotland, and Wales).</p> <p>Original datasets:</p> <p><a href="https://www.electoralcommission.org.uk/find-information-by-subject/elections-and-referendums/past-elections-and-referendums/eu-referendum/electorate-and-count-information">EU Referendum Results</a> - Electoral Commission</p> <p><a href="https://ons.maps.arcgis.com/home/item.html?id=0560301db0de440aa03a53487879c3f5">Rural Urban Classification (2011) of Local Authority Districts in England</a> - Office for National Statistics</p> <p><a href="https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/internationalmigration/datasets/populationoftheunitedkingdombycountryofbirthandnationality">Population of the UK by country of birth and nationality</a> (July 2015 to June 2016) - Office for National Statistics</p> <p><a href="https://webarchive.nationalarchives.gov.uk/20160107125615/http:/www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-and-quick-statistics-for-local-authorities-in-the-united-kingdom---part-1/rft-ks201uk.xls">2011 Census: KS201UK Ethnic group, local authorities in the United Kingdom</a> - The National Archives</p> <p><a href="https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/datasets/claimantcountbyunitaryandlocalauthorityexperimental/current">CC01 Regional labour market: Claimant Count by unitary and local authority (experimental)</a> (20 July 2016) - Office for National Statistics</p> <p><a href="https://www.ons.gov.uk/economy/grossvalueaddedgva/datasets/regionalgvaibylocalauthorityintheuk">Regional GVA(I) by local authority in the UK</a> - Office for National Statistics</p> <p><a href="https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland">Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland</a> (Mid-2016: Superseded) - Office for National Statistics</p> <p><a href="https://webarchive.nationalarchives.gov.uk/20160106225510/http:/www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-and-quick-statistics-for-local-authorities-in-the-united-kingdom---part-2/rft-ks501uk.xls">2011 Census: KS501UK Qualifications and students, local authorities in the United Kingdom</a><br> - The National Archives</p>
Underlying data of the project "Are CONSORT checklists submitted by authors adequately reflecting what information is actually reported in published papers?"
<p><em><strong>Supplementary file 3.xlsx</strong></em> contains the evaluations for the 12 papers included in the study.</p>
EOL Dynamic Hierarchy: EOL Dynamic Hierarchy 2.1 with eolIDs, authorities & higher classification
Currently active Dynamic Hierarchy and archived versions. For more information, see: <p></p>https://eol.org/docs/eol-dynamic-hierarchy<p></p>The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Earth BioGenome Project: Authors and author contributions for publication "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life"
<p>The zipped file cntains three tab-separated datasets (worksheets). These worksheets list</p> <p>The contributing authors for the manuscript "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" and the roles of these authors in the manuscript.</p> <p>The funding sources for these authors</p> <p>A list of the authors contributing to the "EBP Community of Scientists" collective authorship for the same manuscript.</p>
Genome-wide comparison reveals large structural variants in the cassava landraces Authors
<p><span>Structural variants (SVs) are critical for plant genomic diversity and phenotypic variation. This study investigates a large, 9.7 Mbp highly repetitive segment on chromosome 12 of <em><span>TMEB117</span></em>, a region not previously characterized in cassava. We aim to explore its presence and variability across multiple cassava landraces, providing insights into its genomic significance and potential implications.</span></p>
CF_D1 Interviews with public authorities about climate adaptation finance
<p><span>The CLIMATEFIT dataset D1 contains data </span><span>collected</span><span> as part of </span><span>WP1’s</span> <span>T</span><span>ask 1.1</span><span>: </span><span>Assess financing barriers and drivers for territories</span><span> (Leader: </span><span>Actierra</span><span>). <span>This dataset contains interview transcripts and summaries of interviews conducted by the facilitators with the 20 territories</span>. The interviews were about challenges and barriers regarding accessing climate adaptation funds and sources.</span></p> <p><span>Data that is already published in WP1’s <a href="https://climatefit-heu.eu/knowledge-center/">Deliverable 1.1: Adaptation Investment Landscape</a>, is not included in this dataset. </span></p> <p><span>More information can be found in the Readme file in this dataset.</span></p>
SNAP Authorized dollar stores
<p>SNAP authorized dollar stores</p>
Quality and Trackability of Author Indicated Software Citations (NEST Case Study)
<p>This dataset contains a randomly selected minimal sample of 471 NEST software citations, which were provided by authors and published on the NEST software page as a publication list. The software citations were analyzed on their quality and trackability.</p> <p>The data was collected in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Author Name Disambiguation using Markov Chain Monte Carlo (MCMC)
<p>This repository contains the files processed from the <a href="https://zenodo.org/record/5675801#.Y2APcOzMJhE">Aminer-534K</a> Knowledge graph for the master's thesis on Author Name Disambiguation.</p>
Comparative analysis of health authorities spokesperson and health influencer during the COVID-19 pandemic: A case in Indonesia
<p><span><strong>Background</strong>: </span><span>Concerns over an infodemic following a surge in health misinformation circulating on social media sets out the government's priority for Indonesia. Given the urgent work on the coronavirus disease 2019 (COVID-19) response, the government collaborated with health-related spokespersons and influencers with a medical background by starting a COVID-19 public education campaign on social media. A collaborative initiative involved health spokespersons from government and non-government to clarify misinformation about COVID-19.</span></p> <p><span><strong>Methods</strong>: </span><span>The primary purpose of this research is to compare government and non-government spokespersons by examining their role in educating about the COVID-19 vaccine and health services. This study employed comparative factor analysis and non-participatory observation toward the media activity of spokespersons in Indonesia. Using a questionnaire, this study examines the dimensions of public campaigns, risk communication, health and emergency, leadership, and communication from Indonesian spokespersons. The data collection was conducted in two stages. The first stage was a pilot study that collected data from 102 respondents, the second stage collected data from 276 respondents.</span></p> <p><span><strong>Results</strong>: </span><span>Findings show that utilizing the spokesperson is important due to its capabilities of reaching diverse audiences, and improving public engagement, trustworthiness, and credibility.</span></p> <p><span><strong>Conclusions</strong>: </span><span>With the combination of health authorities spokespersons and health influencers in Indonesia, this study provides valuable insights for communication management in developing and supporting the role of health authorities from the government, non-government as well as medical sectors.</span></p>
The versatility of pulses: Are consumption and consumer perceptions in different European countries related to the actual climate impact of different pulse types? Author links open overlay panel
<p>Pulses support sustainable production and consumption. Their culinary versatility creates a wide range of possibilities for new products, bridging consumers’ preparation barriers. However, this potential is often intangible for consumers who have little knowledge about plant-based foods. Based on an online survey in Denmark, Germany, Poland, Spain, and the United Kingdom (<em>N</em> = 4,226), this study aimed to investigate consumer utilization and perception of pulses as a versatile, low-carbon food relative to objective life cycle assessment (LCA) measures of 12 pulse types. The most popular pulse types, with specific preferences across countries, were lentils, kidney beans, and chickpeas, typically consumed at home and purchased in dried or canned form. Respondents associated pulses with being healthy and natural, but sustainability was not an essential attribute related to the perception of pulses. LCA revealed a low environmental impact caused by pulse production and consumption, with marginal variations between types and produce. Respondents were unaware of the nuances in the environmental impact of different pulse types, generally perceiving uncommon pulses to be relatively more sustainable than others. In conclusion, a low consumption combined with a misconception of pulses’ environmental impact may demand different promotional strategies including clear communication to inform consumers.</p>
BUKTI CORRESPONDING AUTHOR IGNATIA MARTHA HENDRATI Email: ignatia.hendrati.ep@upnjatim.ac.id Journal Cogent Business and Management The Role of Moderation Activities The Influence of The Audit Committee And The Board Of Directors on The Planning of The Sustainability Report
<p><strong>The Role of Moderation Activities The influence of the Audit Committee and the Board of Directors on the Planning of the Sustainability Report</strong></p> <p><strong>Abstract</strong></p> <p>In order to show the consistency of agency theory as a theory to explain the influence of the Audit Committee and the Board of Directors on Sustainability, this study will explore the role of moderating actions of the Audit Committee and the Board of Directors on Sustainability. The firms that make up the demographic and research sample for this study, which uses a quantitative technique, are those that are included in the Jakarta Islamic Index for the years 2017 through 2021. the study's yearly financial report panel data. The data analysis methods employed in this study were robust, fixed effects, random effects, and ordinary least square regression. These methods are one of the regression solution approaches that may be used with a lot of flexibility in research that combines thoughts, ideas, and facts. The first study found that the audit committee had an effect on sustainability, whereas the second found that the board of directors has no effect. Due to the third and fourth conclusions of the role of activities, the audit committee and board of directors are less strong on sustainability.</p> <p><strong>Keywords </strong>: Activity; Board of Directors; Sustainability; Audit Committee; Stata</p> <p><strong>JEL Classification: </strong>G32, G02, M1, G34, Z1</p>
Publications from government weather and climate authorities on the X platform and replies from citizens in five Brazilian cities during a year between 2021 and 2022
<p>This dataset contains messages published and replies received by government weather and climate authorities on the X (former Twitter) social media platforms. The data comprises government weather and climate authorities for the Brazilian cities of São Paulo, Rio de Janeiro, Belo Horizonte, Porto Alegre, and Belém. Government weather and climate authorities are city hall departments or sectors responsible for informing and keeping the population updated about weather events.</p><p>Publications made by the authority and replies published by citizens to these publications are observed. This data supports the study on the interaction dynamics between the climate authority and citizens over time.</p><p><strong>Data Structure</strong></p><p>Two files are available <strong>publications.csv</strong> and <strong>replies.csv</strong>.</p><p>Each line in the publications' file (<strong>publications.csv</strong>) refers to an authority publication/tweet. For each publication, it is stored the public authority's unique Twitter identifier (<i>AUTHORITY_ID</i>), the tweet unique identifier (<i>TWEET_ID</i>), the Unix timestamp that indicates when it was published (<i>TIMESTAMP</i>), and the text of the publication (<i>TEXT</i>). </p><p>Each line in the replies file (<strong>replies.csv</strong>) is a reply from a citizen to an authority. For each reply, it is stored the authority's unique Twitter identifier (<i>AUTHORITY_ID</i>), the unique identifier of the authority's tweet being replied to (<i>TWEET_ID</i>), the replier masked unique Twitter identifier (<i>AUTHOR_ID</i>), and the reply Unix timestamp (<i>TIMESTAMP</i>) that indicates when it was published.</p><p>All data were collected through the X's application programming interface (API) provided to scientific researchers. Publications and replies were posted by users (authorities and citizens) with public visibility.</p><p><strong>Data Content</strong></p><p>The dataset covers 1-year observation period, starting on July 17, 2021, and ending on June 16, 2022. It contains a total of 10,229 publications and 5,471 replies. The observed authorities are as follows:</p><p> </p><p><strong>City Authority name X handle </strong><i><strong>AUTHORITY_ID</strong></i></p><p>São Paulo Centro de Gerenciamento de Emergências Climáticas da Prefeitura de SP <i>@cge_sp </i>268407434</p><p>Rio de Janeiro Sistema de Alerta localizado no Centro de Operações do Rio (COR) @<i>alertario </i>87487749</p><p>Belo Horizonte Defesa Civil de Belo Horizonte @<i>defesacivilbh </i>837731966</p><p>Porto Alegre Defesa Civil Porto Alegre @<i>defesacivilpoa </i>1037420896473022466</p><p>Belém Defesa Civil de Belém @<i>defesacivilbel </i>1346501728632500225</p><p> </p><p>As weather and climate authorities are government bodies, the whole content of their publications is of public interest according to Brazilian law. Thus, the text messages in their publications on social media are in the public domain and are stored in this dataset. As the data structure describes, text messages of citizens' replies are not stored. According to the terms of use of the X platform, citizen text messages cannot be publicly stored outside the X platform. Such text messages are public on that platform, and, for reproductivity, they can be recollected using the platform web page or API informing the <i>TWEET_ID</i> stored in this dataset.</p>
Disambiguated Author Identity (ID) Dataset for PubMed
<p>This dataset contains disambiguated author identities (IDs) of all authors in PubMed, which are created by our method. A research paper on this method is currently in preparation.</p>
Dataset: Reliability of the domain authority scores calculated by Moz, Semrush, and Ahrefs
<p>This dataset contains the <em>Domain Authority</em>, <em>Authority Score</em>, and <em>Domain Rating </em>collected by using Moz, Semrush, and Ahrefs applications. By doing different searches, the data collection was conducted in Google using keywords selected in the most neutral way possible. The selection of keywords was made by applying two criteria: 1) Fifty words of four or more characters were selected from those identified as being the most frequently used on the web, according to the WordFrequency ranking, and 2) the eleven keywords used most during the previous six months to conduct searches on Google, according to Google Trends in November 2022, were added. The data collection was carried out between 11/03/2022 and 12/22/2022. In total, 16,937 results were obtained.</p> <p>All the searches were conducted simultaneously and in the same geographical location to avoid any potential bias. Subsequently, the URL information corresponding to the path, file name, and parameters was removed, resulting in 6,268 domains. All duplicates were then removed, leaving a final sample of 3,151 distinct domains.</p> <p>This data set accompanies the article entitled "Reliability of the domain authority scores calculated by Moz, Semrush, and Ahrefs".</p>
CSV Dataset Files and JSON OpenRefine Recipes for Alignment of the Schoenberg Dataset of Manuscripts (SDBM) Name Authority with Wikidata
<p>Dataset CSVs and JSON recipe files for OpenRefine for a project to align Name Authority records in the Schoenberg Dataset of Manuscripts (SDBM) with Wikidata Items</p>
OpenAlex Author Name Disambiguation V3 Initial Clusters
<p>Author name disambiguation V3 initial clusters for the OpenAlex dataset. See <a href="https://openalex.org">https://openalex.org</a></p> <p>There are 633803287 rows, split into 4 CSV (comma-delimited) files (with headers).</p> <p>The CSV files have two columns: "work_author_id" and "author_id"</p> <p>"work_author_id": An OpenAlex Work ID and an author sequence number, joined with an underscore ("_")</p> <p>"author_id": An OpenAlex Author ID, representing a unique author in OpenAlex</p>
Balancing Authority Hourly Generation Of Installed Plant Capacities in CONUS
<p>This dataset contains the hourly generation time series for each Balancing Authority representing the installed capacity in each month from 2007 through 2020. The time series is aggregated from plant-level generation of plants that exist in the Energy Information Administration (EIA) database as of 2020. The time series are simulated <strong>actual generation</strong> meaning that the installed capacities have been applied to the hourly capacity factor profiles in <a href="https://doi.org/10.5281/zenodo.7901614">Bracken et al. 2023</a>. These historical generation time series reflect the actual monthly installed capacity at each plant; the EIA 860 and EIA 860m databases were mined to identify months of first operation, retirement, and extended periods of maintenance or non-operation. These databases were also used to create time series of Balancing Authority (BA) level hourly generation to reflect the actual monthly installed capacity. The BA-level time series tracks the BA membership of each power plant in each month; the time series reflects the monthly inventory and hourly production in each BA.</p> <p>For more information please refer to Campbell et al. 2023, Dynamically Downscaled Power Production for All EIA Wind and Solar Power Plants, in prep, and to the code repository at <a href="https://github.com/GODEEEP/godeeep-eia-power">https://github.com/GODEEEP/godeeep-eia-power</a>.</p> <p>The dataset contains three types of files:</p> <p>Monthly Plant-Level Inventory - The monthly plant-level inventory (<code>all_years_860m.csv</code>) contains the identification codes for each generator (including the <code>plant_code_unique</code> identifier to link with the solar and wind profiles in Bracken et al. 2023), the capacity in that month, the balancing authority that plant operated for in that month, the resource type (<code>solar</code> or <code>wind</code>), the month and year, and whether the nameplate capacity in that month needs to be scale to account for aggregation of very large wind power plants (i.e., more than 300 turbines).</p> <p>BA-level Hourly Generation - The BA-level hourly generation files (<code>solar_BA_generation.csv</code> and <code>wind_BA_generation.csv</code>) contain hourly generation at the BA level for each BA in the 2020 EIA 860 database. The first column contains a time stamp and each column header is the name of the BA as it exists in the EIA 860 database (e.g., ISO New England is ISNE, CAISO is CISO). The time series spans 2007 through 2020. The time series begin in 2007, as this aligns with the first year of publication of the EIA 923 monthly plant-level generation dataset and with the first year of available BA-level self-reported generation. The purpose of the temporal baseline alignment is for validation of the time series, discussed in Campbell et al. 2023. Validation metrics in this paper are provided at the BA-level.</p> <p>Plant-level Hourly Generation - The plant-level hourly generation files (<code>solar_plant_generation.csv</code> and <code>wind_plant_generation.csv</code>) contain hourly generation at the generator level for each power plant that exists in the EIA 860 database in 2020. The files are organized with an hourly timestamp for each row and a unique generator id for each column. The generator id is a concatenation of the EIA Plant ID and the EIA Generator ID with an underscore separating the strings. <strong>The plant-level hourly generation time series are intended to be aggregated to the BA-level.</strong> These plant-level time series are provided to the user to allow for re-aggregation for bespoke regional analyses.</p> <p> </p> <p><strong>Known Issues</strong></p> <ul> <li>The following wind power plants (identifier <code>plant_code_unique</code>) have a cf greater than 1 and were scaled to 0.885 <ul> <li>['2024', '2024_1', '2024_3', '2024_4', '7855', '7855_1', '7927', '7927_1', '7927_2', '7965', '7965_1', '7974', '7974_1', '52162', '52163', '54300', '54793', '54793_2', '55741', '55944', '55995_1', '56577', '57214', '57257', '57258', '57258_1', '57594', '57721', '57721_1', '58105', '58112', '58113', '58113_1', '59328', '59329', '59330', '59331', '61677', '61677_1', '61677_2', '62442', '64130']</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <ul> <li>v1.1.0 - Updates the basis for plant-level inventory from the EIA860 monthly reports (considered preliminary) to the EIA860 annual reports (considered complete and final).</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Post-authorization Safety Study in CKD Subjects Receiving HX575 i.v.
ClinicalTrials.gov study NCT00632125. IPD Sharing: UNDECIDED. Countries: 10. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.