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720 results for “Portal”
WikiPathways Nanomaterials Portal
<p>Archive of the WikiPathways Nanomaterials Portal on 8 November 2022. Includes PNG images and original GPML files.</p>
User-centered Usability Analysis of 41 Open Government Data Portals
<p>The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.</p>
Grapegenomics.com: a web portal with genomic data and analysis tools for wild and cultivated grapevines
<p><a href="https://grapegenomics.com">Grapegenomics.com</a> is a web portal that provides public access to genome references for grapevine cultivars (<em>Vitis vinifera</em> ssp. <em>vinifera</em>), wild grapevines (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>), various wild grape species (<em>Vitis</em> spp. and <em>Muscadinia</em> spp.), and major fungal pathogens affecting grapes.</p> <p>All genomes are accessible through dedicated genome browsers, and published genomes are available for complete <a href="https://www.grapegenomics.com/download.php">download</a>.</p> <p>The site hosts all genomes produced by the laboratory of Dario Cantù in the Department of Viticulture and Enology at the University of California, Davis, along with published genome references generated by others, such as PN40024 and Pinot noir ENTAV115. Instructions for genome submission are provided <a href="https://www.grapegenomics.com/submit.php">here</a>. The portal is maintained by Noé Cochetel (ndcochetel[at]ucdavis.edu). In this version 2.0, all genome browsers utilize <a href="https://jbrowse.org/jb2/">jbrowse 2</a>. <br><br>Link to the website: <a href="https://www.grapegenomics.com">https://www.grapegenomics.com</a> </p>
Cathedral (Cathédrale Notre-Dame). Exterior. West facade. South portal. Statues.
<u>File Name</u>: PM_150038_F_Strasbourg <br><u>Sublocation</u>: Cathédrale Notre-Dame <br><u>Location</u>: Strasbourg <br><u>Province</u>: Gand-Est, Bas-Rhin <br><u>Country</u>: France <br><u>Header</u>: La façade méridionale, le portail latéral sud, statues le Tentateur et "Les vierges follessur" le piédroit gauche <br><u>Description</u>: Cathedral (Cathédrale Notre-Dame). Exterior. West facade. South portal. Statues. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Europe|France; Europe|France|Grand Est; Europe|France|Grand Est|Bas-Rhin; Europe|France|Grand Est|Bas-Rhin|Strasbourg; Cultural heritage|Monuments|Cathedral; Cultural heritage|Monuments; Cultural heritage <br><u>Date of Generation</u>: 2023-08-14T13:20:51+02:00
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
ShareTrait: a data portal for making trait data interoperable and reusable
<p><strong>Scope</strong></p> <p>This dataset provides the SQL version of the ShareTrait database, corresponding to <strong>ShareTrait version 1.2.0</strong>. It represents the structured database equivalent of the previously published dataset, <strong>ShareTrait: a data portal for making trait data interoperable and reusable</strong>, version 1.0.0 (Zenodo, July 12, 2023, <a href="https://doi.org/10.5281/zenodo.8138904" target="_new" rel="noopener">DOI: 10.5281/zenodo.8138904</a>). This version includes the database file, an SQL query, and the resulting output file generated from the SQL query. </p> <p><strong>Dataset content</strong></p> <p>The repository provides:</p> <ul> <li><strong>ShareTrait-database-v1.2.0.db</strong> : the database file.</li> <li><strong>master-query-all.sql</strong> : SQL query used for reconstructing the dataset.</li> <li><strong>master-query-output.csv</strong> : SQL query output file .</li> <li><strong>ShareTrait-dataset-database-mapping.csv</strong> : mapping of attribute fields from the SQL query output to the corresponding attributes in <a href="/records/8138904/files/ShareTrait_MetaData_v1.0.0.csv?download=1">ShareTrait_MetaData_v1.0.0.csv</a>, with relevant column name = "Column_name", version 1.0.0 (<a href="https://doi.org/10.5281/zenodo.8138904" target="_new" rel="noopener">DOI: 10.5281/zenodo.8138904</a>)</li> </ul> <p><strong>Access and usage</strong></p> <p>Files and documentation are available on GitHub: <a href="https://github.com/ShareTraitProject" target="_new" rel="noopener">ShareTrait Project</a>. Users can execute the SQL query by using either SQLiteStudio or SQLite3. Detailed instructions are provided in this repository <strong>README</strong>.</p> <p><strong>Citation</strong></p> <p>When using this dataset, please provide the following citation:</p> <p>Leiva, F., Ellers, J., Berg, M. P., Cuxart-Erruz, R., Barneche, D., Blackburn, T., Castañeda, L. E., Chown, S., Gaitán-Espitia, J. D., Gebauer Mery, P. H., Gomez Isaza, D., Hardy, I., Hermaniuk, A., Hirst, A., Jorissen, S., Keasar, T., Koene, J. M., Le Lann, C., Martorelli, I., Molinet C., Niklitschek E.J., Oliveira B., Olivier B., Orizaola G., Pilakouta N., Shameer K.S., Shokri M., Stoks R., Tougeron K., Tuni C., van de Pol I.L.E., van Dis N.E., Visser B., Vogels J., White C.R., Wu N., and Verberk W. (2025). ShareTrait: a data portal for making trait data interoperable and reusable (version 1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14826294" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.14826294</a></p> <p>For further information, please contact: <a rel="noopener">i.martorelli@vu.nl</a></p>
Smarter open government data for Society 5.0: analysis of 51 OGD portals
<p>This dataset contains data collected during a study <a href="https://doi.org/10.3390/s21155204">"Smarter open government data for Society 5.0: are your open data smart enough"</a> (<em>Sensors</em>. 2021; 21(15):5204) conducted by Anastasija Nikiforova (University of Latvia).<br> It being made public both to act as supplementary data for "Smarter open government data for Society 5.0: are your open data smart enough" paper and in order for other researchers to use these data in their own work.</p> <p>The data in this dataset were collected in the result of the inspection of 60 countries and their OGD portals (total of 51 OGD portal in May 2021) to find out whether they meet the trends of Society 5.0 and Industry 4.0 obtained by conducting an analysis of relevant OGD portals.</p> <p>Each portal has been studied starting with a search for a data set of interest, i.e. “real-time”, “sensor” and “covid-19”, follwing by asking a list of additional questions.<br> These questions were formulated on the basis of combination of (1) crucial open (government) data-related aspects, including open data principles, success factors, recent studies on the topic, PSI Directive etc., (2) trends and features of Society 5.0 and Industry 4.0, (3) elements of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use Model (UTAUT).</p> <p>The method used belongs to typical / daily tasks of open data portals sometimes called “usability test” – keywords related to a research question are used to filter data sets, i.e. “real-time”, “real time” and “real time”, “sensor”, covid”, “covid-19”, “corona”, “coronavirus”, “virus”. In most cases, “real-time”, “sensor” and “covid” keywords were sufficient.<br> The examination of the respective aspects for less user-friendly portals was adapted to particular case based on the portal or data set specifics, by checking:<br> 1. are the open data related to the topic under question ({sensor; real-time; Covid-19}) published, i.e. available?<br> 2. are these data available in a machine-readable format?<br> 3. are these data current, i.e. regularly updated? Where the criteria on the currency depends on the nature of data, i.e. Covid-19 data on the number of cases per day is expected to be updated daily, which won’t be sufficient for real-time data as the title supposes etc.<br> 4. is API ensured for these data? having most importance for real-time and sensor data;<br> 5. have they been published in a timely manner? which was verified mainly for Covid-19 related data. The timeliness is assessed by comparing the dates of the first case identified in a given country and the first release of open data on this topic.<br> 6. what is the total number of available data sets?<br> 7. does the open government data portal provides use-cases / showcases? <br> 8. does the open government portal provide an opportunity to gain insight into the popularity of the data, i.e. does the portal provide statistics of this nature, such as the number of views, downloads, reuses, rating etc.?<br> 9. is there an opportunity to provide a feedback, comment, suggestion or complaint?<br> 10. (9a) is the artifact, i.e. feedback, comment, suggestion or complaint, visible to other users?</p> <p>***Format of the file***<br> .xls, .ods, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</p>
AquaMatch Chlorophyll a Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Chlorophyll a Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“v2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat chlorophyll a dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we can not verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 6, 1970, to June 20, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Research, 5
AquaMatch Dissolved Organic Carbon Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Dissolved Organic Carbon Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat dissolved organic carbon dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we cannot verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 5, 1970, to June 27, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water
AquaMatch Total Suspended Solids from Water Quality Portal ~1970-2025
This dataset, “AquaMatch Total Suspended Solids Data from Water Quality Portal ~1970-2025”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat Total Suspended Solids ("TSS") dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we cannot verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 10, 1970, to April 9, 2025 from the conterminous US, Alaska, Hawaii, American Samoa, Puerto Rico, United States Virgin Islands, Guam, and Commonwealth of the Northern Mariana Islands. It contains 335,099 records from locations classified as estuaries; 411,979 records from locations classified as lakes, reservoirs, and impoundments; and 3,858,957 records from locations classified as streams. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National
Precio del alquiler en Alicante y Madrid por Barrio en el Portal Fotocasa
<p>Dataset referente al precio del alquiler de vivienda en las ciudades de Madrid y Alicante. </p> <p>Contiene los siguientes atributos:</p> <ol> <li><em>BuildingType</em>: Tipo de inmueble (Piso, Garaje, Local comercial, etc..)</li> <li><em>BuildingSubtype</em>: Tipo de vivienda (Piso, Ático, Loft, etc..)</li> <li><em>ClientAlias</em>: Nombre del usuario/ Inmobiliaria que gestiona el alquiler</li> <li><em>Location</em>: Localización; Calle o barrio </li> <li><em>Price</em>: Precio mensual en Euros</li> <li><em>Latitude</em>: Latitud (ubicación)</li> <li><em>Longitude</em>: Longitud (ubicación)</li> <li><em>City</em>: Ciudad</li> <li><em>District</em>: Distrito</li> <li><em>Neighborhood</em>: Barrio</li> <li><em>zipCode</em>: Código postal</li> <li><em>Bathrooms</em>: Numero de baños</li> <li><em>Rooms</em>: Número de habitaciones</li> <li><em>Surface</em>: Superficie en m2</li> </ol> <p> </p>
Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022
<p>Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as <em>Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022</em> have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.</p> <p>Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.</p> <p>The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.</p> <p>Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.</p> <p>More information on our methods, data sources, and data processing and analysis codes are found on our <a href="https://github.com/C-GRASP">github page </a></p> <p>The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files</p> <ol> <li>dataset_10kmcoast.csv- This is all data that is found to be within 10km of the Natural Earth coastline polyline</li> <li>Data_EstimatedOnshore.csv- This is all the data from dataset_10kmcoast.csv that lies within the Natural Earth United States Polygon</li> <li>Data_VerifiedOnshore.csv- This is all data that was able to be verified onshore from either sampling method, note, or location type data</li> <li>Data_Post2012_VerifiedOnshore.csv- This is all the data from Data_VerifiedOnshore.csv that is after 2012</li> </ol> <p>The files each have the following fields (no data is blank):</p> <p>'ID': row ID integer</p> <p>'Sample_ID': identifier to raw data source</p> <p>'Sample_Type_Code': code of sample id</p> <p>'Project': raw datasource project identifier</p> <p>'dataset': raw dataset major identifier</p> <p>'Date': date, where specified, and to whatever precision that is specified</p> <p>'Location_Type': where specified, code indicating type of location information</p> <p>'latitude': latitude in decimal degrees</p> <p>'longitude': longitude in decimal degrees</p> <p>'Contact': where specified, raw data originator</p> <p>'num_orig_dists': number of unique grain size distributions</p> <p>'Measured_Distributions': number iof measured grain size distributions</p> <p>'Grainsize': grain size is sometimes reported without specification</p> <p>'Mean', mean grain size in mm</p> <p>'Median', median grain size in mm</p> <p>'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])</p> <p>'Kurtosis', kurtosis value (non-dim)</p> <p>'Kurtosis_Class', kurtosis category</p> <p>'Skewness', skewness value (non-dim)</p> <p>'Skewness_Class', skewness category</p> <p>'Std', standard deviation of grain sizes </p> <p>'Sorting', sorting category</p> <p>'d5', grain size distribution 5th percentile</p> <p>'d10', grain size distribution 10th percentile</p> <p>'d16', grain size distribution 16th percentile</p> <p>'d25', grain size distribution 25th percentile</p> <p>'d30', grain size distribution 30th percentile</p> <p>'d50', grain size distribution 50th percentile</p> <p>'d65', grain size distribution 65th percentile</p> <p>'d75', grain size distribution 75th percentile</p> <p>'d84',grain size distribution 84th percentile</p> <p>'d90', grain size distribution 90th percentile</p> <p>'d95', grain size distribution 95th percentile</p> <p>'Notes': notes - these can be informative and substantial, do not disregard</p> <p> </p> <p>Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format</p>
Onboarding to EOSC Portal introduction video for PolicyCLOUD
<p>This presentation video is a first stop for any organisation interested in becoming a European Open Science Cloud (EOSC) resource provider. We will see what are the key components of EOSC, the benefits to onboard Providers and Resources to the EOSC Portal, what is the criteria to be validated as a resource provider, and links to additional step by step instructions to complete your onboarding as a resource provider. </p>
Nicosia, Cyprus. Bedesten, north side, eastern-most portal.
<p>Nicosia, Cyprus. Bedesten, north side, eastern-most portal, as documented in 1974 (after removal of shops).</p>
Usability of Open Data Portals
<p><span>This dataset reports on the usability of Open Data Portals as reported by the European public. Knowledge of open science and data concepts is also reported. </span></p>
Resource Metadata Harvested from Government and Research Open Data Portals
<p>This dataset consists of resource metadata harvested from the APIs of hundreds of government and research data portals from all over the world. This dataset was harvested between the 13<sup>th</sup> and 15<sup>th</sup> of September 2018. The metadata harvested from these portals was translated to a single metadata format (see <em>metadata_format.odt</em>). An overview of all harvested domains is given in <em>portal_list.txt</em>.</p> <p>The harvested data is divided into five gzipped json-lines files, based on the ‘type’ of the resource that is derived from the data of the APIs:</p> <ul> <li><em>dataset_metadata.jsonl.gz</em>: Resources classified as a Dataset, or subsets of dataset (e.g. Dataset:Image and Dataset:Audio) [6 246 250 resources]</li> <li><em>document_metadata.jsonl.gz</em>: Resources classified as a Document, or subset of document (e.g. Document:Paper:Conference and Document:Book) [15 626 541 resources]</li> <li><em>software_metadata.jsonl.gz</em>: Resources classified as Sofware (including Software:Model) [42 036 resources]</li> <li><em>service_metadata.jsonl.gz</em>: Resources classified as a service (e.g. WMS, APIs) [1257 resources]</li> <li><em>other_metadata.jsonl.gz</em>: Resources of which the ‘type’ could not be determined from the data the API returned. This set still contains many datasets [1 502 979 resources]</li> </ul>
Dataset: Comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research.
<p>Supplementary material for a comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research. We conducted a relevance evaluation with 6 users over 19 search questions in two search interfaces.</p> <p>The users provided up to five search questions and relevant keywords from their research background. We setup a dataset search over a corpus of ~92,000 randomly selected metadata files from GFBio (<a href="https://www.gfbio.org">https://www.gfbio.org</a>). For each of their own search queries, the users got two result sets presented. The first one displayed results obtained from a keyword search. The second panel contained dataset results from a prototypical semantic search. Instead of results with exact mentions of the query terms, the semantic search also presented related results with synonyms and more specific terms or terms obtained from concept nodes of a higher hierarchy level.</p> <p>Each user rated the relevance of his/her own search queries on a 7-point Likert scale for both search results.<br> In addition, users also assessed the expanded keywords for each question.</p> <p>More information can be found in our publication:</p> <p>Löffler, F. and Klan, F. (2016): Does Term Expansion Matter for the Retrieval of Biodiversity Data? in Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16), co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016),2016, <a href="http://ceur-ws.org/Vol-1695/paper2.pdf">http://ceur-ws.org/Vol-1695/paper2.pdf</a></p> <p> </p>
Blair et al. 2020: Machine learning identification of ground beetles (repackaging of occurrences published by the NEON Biorepository Data Portal)
Blair, J.; Weiser, M. D.; Kaspari, M.; Miller, M.; Siler, C.; Marshall, K. E. 2020. Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale. Ecology and Evolution 10 (23): 13143-13153. https://doi.org/10.1002/ece3.6905 Additional NEON samples (not yet archived at the Biorepository) were used in this research: full list of occurrences used.
Stachewicz et al. 2021: Trait correlation, phylogenetic signal in Carabidae morphology (repackaging of occurrences published by the NEON Biorepository Data Portal)
Stachewicz JD, Fountain-Jones NM, Koontz A, Woolf H, Pearse WD, Gallinat AS. 2021. Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology bioRxiv 02.12.431029; doi: https://doi.org/10.1101/2021.02.12.431029 Many NEON samples and specimens used in this work resulted from NEON prototype data and will not be archived in the Biorepository. See the appendices in the above linked article for a full list of NEON samples and specimens and their associated collection data. Additionally, see appendices of above linked article for specimen-level morphological trait measurements and genetic sequence data.
Staines & Staines 2021: The Geadephaga (Coleoptera: Carabidae and Rhysodidae) of SERC (repackaging of occurrences published by the NEON Biorepository Data Portal)
Linked records of Carabidae and Rhysodidae specimens from the SERC site, collected between 2015-2018, included in the following publication: Staines, C.L. & Staines, S. L. The Geadephaga (Coleoptera: Carabidae and Rhysodidae) of the Smithsonian Environmental Research Center, Maryland. 2021. Banisteria 55: 75-100. Original abstract: "An inventory of the Geadephaga (Coleoptera) at the Smithsonian Environmental Research Center, Anne Arundel County, Maryland is being conducted. Pitfall traps were placed and monitored from 2015 to 2018. From 2017 to 2020 directed collecting efforts were made to document the Geadephaga of the facility. A total of 111 Geadephaga species was collected: Carabidae - 110, Rhysodidae - 1." Research article available for download: https://virginianaturalhistorysociety.com/banisteria/pdf-files/ban55/Staines_SERC_Geadephaga.pdf
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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.