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.
3,200
datasets available to search
ShareScore release 0.9.0
Dataset results
3,200 results for “indexicality”
Refractive index obtained from thin films of Au-Ag alloys
<p>This is the optical dataset for Au-Ag alloys, obtained in the paper "Optical properties of Au-Ag alloys: An ellipsometric study" [Opt. Mater. Express 4 (2014) 403-410].</p>
Level A Pan Europe Solar Index for estimation of Potential evaporation February
Solar Index for estimation of Potential evaporation February. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation August
Solar Index for estimation of Potential evaporation August. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation March
Solar Index for estimation of Potential evaporation March. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module)
Level A Pan Europe Solar Index for estimation of Potential evaporation July
Solar Index for estimation of Potential evaporation July. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation Apr
Solar Index for estimation of Potential evaporation April. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation November
Solar Index for estimation of Potential evaporation November. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation September
Solar Index for estimation of Potential evaporation September. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation May
Solar Index for estimation of Potential evaporation May. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation June
Solar Index for estimation of Potential evaporation June. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation October
Solar Index for estimation of Potential evaporation October. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation January
Solar Index for estimation of Potential evaporation January. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation December
Solar Index for estimation of Potential evaporation December. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Community assets and relative rurality index
<p>The CARR (Community assets and relative rurality index) index is a continuous, multidimensional measure of rurality based on the concept of sustainable development that integrates measures of environmental, social, and economic resources. The index incorporates traditional rurality measures such as population density, distance from metropolitan areas, and developed area with measures of availability and accessibility of 70 different infrastructure types that support essential community services and community amenities. The index uses data last updated in 2019 and is available for census block-groups in the coterminous United States.</p> <p>This repository includes versions of CARR calculated for block groups (original) and versions of CARR aggregated to other conventional spatial units using and area-weighted averaging approach on the original block group data. Each zip folder includes the raw data, a metadata file, and a basic exploratory data analysis document in html format.</p> <p>The information for Version 2 of this repository, and additional information on CARR, can also be found at <a href="ruralityindex.com" target="_blank" rel="noopener">ruralityindex.com</a>.</p>
Data and code for "Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign"
<p>Datasets nd codes that have been used for the paper with tittle "Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign"</p>
System Matrices and Matlab Code for Loewner for Index-2 Systems
<p>This code accompanies the paper</p><blockquote><p><i>Implicit and explicit matching of non-proper transfer functions in the Loewner framework</i> by Ioan Victor Gosea and Jan Heiland</p></blockquote><p>that we submitted for presentation at the CDC 2024.</p>
Scientometric Indexes of Scientific Journals
<p>Scientometric data from 62,856 journals consolidated from Scopus, SJC, Diamond Journals, ISSN database, Google Scholar and Brazilian QUALIS (2022). Its purpose is to support researchers who want to analyze and compare various scientometric attributes and also the economic model used by scientific journals around the world.</p><p>CSV UTF-8 File, separated by semicolon.</p><p><strong>Metadata:</strong></p><p>ISSN-L: Journal Identified used by ISSN</p><p>ISSN: Secondary Journal identifier (used for Print or other medium versions);</p><p>Name is in the journal origin language;</p><p>"ASJC" is the Knowledge Area Code based on All Science Journal Category used by Citescore/Scopus;</p><p>"Citescore" is based on 2022 Scopus;</p><p>"Google H5" are based on 2020 database;</p><p>"Qualis" is the Brazilian score calculated for the 2017-2021 quadrienal;</p><p>Country Name (País) is in English merged from all databases;</p><p>"Modelo" can have 3 values: D-Diamond Journals (No money involved), T-Closed (You have to pay to access), A-APC (You have to pay to publish);</p><p>This is a working in progress and we intend to add more atributes and fill some gaps on knowledge areas field (ASJC) and other empty attributes.</p>
SAQI: An Ontology based Knowledge GraphPlatform for Social Air Quality Index
<p>This dataset consists of all contributions made by Social AQI (SAQI) project. The description of dataset is as below -</p> <p>Local Sensor Data (hyperlocal-air-quality-sensor-data) - contains all sensors values recorded through local neighbourhood sensors throught the length of the project <br> Locations for all these sensors are as below - In Najafgarh, Delhi, India : Jharoda Kalan, Nangli Dairy and DTC Bus terminal.<br> In Okhla : Sanjay Colony, Tekhand, Shaheen Bagh.</p> <p>Data from <a href="https://cpcb.nic.in/">Central Pollution Control Board</a> (central-air-quality-sensor-data) - Najafgarh_CPCB.csv, Okhla_CPCB.csv : Contains data provided by CPCB from Najafgarh,Delhi and Oklha, Delhi</p> <p><br> PollutionODP.owl : Ontology Design Pattern for pollution - http://ontologydesignpatterns.org/wiki/Submissions:Pollution.<br> <br> Ontology : SAQI ontology as triples (ttl), xml (rdf) and json-ld (json) serialization format<br> Ontology documentation : ontology/diagram contains figures describing ontology, ontology/documentation/saqi.html contains LODE documentation for the ontology</p> <p><br> ethnographic-survey-data - anonymized survey responses for initial pollution perception and literacy survey as well as SAQI app feedback survey.</p> <p>SHACL-shapes - for validating against SAQI ontology.</p> <p> sparql-queries - sample queries to run on our ontology.</p> <p>setup-rdf-store-script - script to setup rdf store with given data using rml mapper.</p> <p> </p> <p><br> </p>
Water Quality Index from Tiete River
<p>Data from WQI (response variable) and 9 physical, chemical, and biological parameters (independent variables), namely: hydrogen potential (pH), dissolved oxygen (do), biochemical oxygen demand (bod), thermotolerant coliforms (tc), total nitrogen (tn), total phosphorus (tp), total solids (ts), turbidity (turb), temperature (temp) were obtained from reports issued by the Environmental Company of the State of São Paulo (CETESB). Annual report available at the link (<a href="https://cetesb.sp.gov.br/aguas-interiores/publicacoes-e-relatorios/">https://cetesb.sp.gov.br/aguas-interiores/publicacoes-e-relatorios/</a>). On this website the documents are available only in Portuguese and from 1978. The WQI used presents a range between 0 and 100, divided into ranges of values and by color: very bad (0 and ≤ 19, purple); bad (> 19 and ≤ 36, red); medum (> 36 and ≤ 51, yellow); good (> 51 and ≤ 79, green) and excellent (> 79, blue). Its object of study data obtained from 78 measurement points located on the Tiete River and its tributaries, from 1994 to 2019. </p><p><strong>The database is part of the doctoral thesis, which addresses the use of machine learning techniques in classifying and predicting water quality indicators.</strong></p><p><strong>CRediT authorship contribution statement: Mario Elias Carvalho do Nascimento: </strong>Conceptualization, Software, Formal analysis, Investigation, Data Curation. <strong>Ralpho Rinaldo dos Reis</strong>: Supervision, Project administration. </p><p><strong>For any inquiries, please contact </strong>marioelias_carvalho@yahoo.com. The .csv will be updated as required to correct issues or to add data from additional surveys. Please check for updated versions periodically.</p><p>Data description:</p><ul><li><strong>ugrhi:</strong> Water Resources Management Units</li><li><strong>macro_land_use:</strong> Classified according to macro land use agriculture, conservation, industrialization, and industrial</li><li><strong>measurement_points: </strong>Measurement points located on the Tiete River and its tributaries</li><li><strong>pH</strong>: hydrogen potential [ ]</li><li><strong>do</strong>: dissolved oxygen [mg/L]</li><li><strong>bod</strong>: biochemical oxygen demand [mg/L]</li><li><strong>tc</strong>: thermotolerant coliforms [NMP/100ml]</li><li><strong>tn</strong>: total nitrogen [mg/L]</li><li><strong>tp</strong>: total phosphorus [mg/L]</li><li><strong>ts</strong>: total solids [mg/L]</li><li><strong>turb</strong>: turbidity [UNT]</li><li><strong>temp</strong>: temperature [ºC]</li><li><strong>wqi</strong>: Range of water quality index [ ]</li><li><strong>classification</strong>: Water quality classes [very_bad, bad, medium, good, excelent]</li></ul><p> </p>
Fig. 5. Mean Gonadosomatic Index for C in Color pattern variation in Cichla temensis (Perciformes: Cichlidae): Resolution based on morphological, molecular, and reproductive data
Fig. 5. Mean Gonadosomatic Index for C. temensis variants grouped by CPV grade. a) Females from the Igapó Açú (Region 1). b) Females from the rio Caures (Region 2). c) Males from the Igapó Açú region. d) Males from the rio Caures. A significant correlation between GSI and CPV Grade was found for males and females in both collecting regions, p <.01 for a, b, d, and d.
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.