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867 results for “repositories”
RDF Reification Benchmark (REF) using the Biomedical Knowledge Repository (BKR)
<p>This resource can be used for benchmarking different RDF modelling solutions for statement-level metadata, namely: </p> <p>- RDF Reification,</p> <p>- Singleton Property,</p> <p>- RDF* (RDF-star). </p> <p> </p> <p>More details about this resource can be found in the following publication:</p> <p>Fabrizio Orlandi, Damien Graux, Declan O'Sullivan, "Benchmarking RDF Metadata Representations: Reification, Singleton Property and RDF*", <em>15th IEEE International Conference on Semantic Computing (ICSC)</em>, 2021.</p> <p>Pre-print available at: http://fabriziorlandi.net/pdf/2021/ICSC2021_REF-Benchmark.pdf</p> <p> </p> <p>The dataset contains 3 different versions of the Biomedical Knowledge Repository (BKR) knowledge graph, as described in:</p> <p>Vinh Nguyen, Olivier Bodenreider, Amit Sheth. "Don't Like RDF Reification? Making Statements About Statements Using Singleton Property" WWW 2014, doi: 10.1145/2566486.2567973.</p> <p>and,</p> <p>Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and Krishnaprasad Thirunarayan. "Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data" in Sci Stat Database Manag. 2010; 6187: 461–470. doi: 10.1007/978-3-642-13818-8_32</p> <p> </p> <p>The 3 knowledge graphs dumps are packaged as Gzipped RDF files in Turtle (and Turtle*) syntax. </p> <p>BKR-R-fullKGdump.ttl.gz for the Reification method,</p> <p>BKR-S-fullKGdump.ttl.gz for the Singleton method,</p> <p>BKR-star-fullKGdump.ttls.gz for the RDF* (RDF-star) method.</p> <p> </p> <p>The RDF REiFication Benchmark (REF) includes also a set of SPARQL (and SPARQL*) queries that can be used to compare the performance of different triplestores.</p> <p>Details about the SPARQL queries, and the queries themselves, are included in the "REF-Benchmark.tar.gz" archive. The queries are named after the dataset they are designed for (BKR-R or BKR-S or BKR-star), plus they include a letter identifying the query set, and a query number. </p> <p>E.g. the query in the file "BKR-R_F-Q3.rq" is for the BKR-R (standard reification) dataset, it is part of the query set "F" and it is the number 3 of that set "F". Hence, the same query, but translated for the RDF* dataset in SPARQL* syntax, is contained in "BKR-star_F-Q3.rq".</p> <p>Sets "A" and "B" are derived from the queries introduced by V. Nguyen et al. in: "Don't Like RDF Reification? Making Statements About Statements Using Singleton Property" WWW 2014, doi: 10.1145/2566486.2567973. Set "F" has been designed more with RDF* in mind as part of this benchmark (see [Orlandi et al., ICSC 2021]) </p> <p> </p> <p> </p> <p> </p>
EPSRC HEED Data Repository: Lantern Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed Lantern Monitoring Systems in Nyabiheke camp, Rwanda. The aim was to (a) evaluate lantern usage pattern – static or mobile (b) evaluate lantern charge and discharge pattern to understand consumption behaviour. The mobile lantern monitors comprise of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device is the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL is used to generate activity and freefall interrupts based on acceleration readings when the lantern is in use. These interrupts are, in turn, processed to calculate the step count of the user. Additionally, the voltage of lantern battery is measured using an in-house designed voltage monitor to evaluate the discharging and charging patterns. The updated values of step count, rate of change of steps and device and lantern battery voltage are stored only if a significant change in the step count is detected. The device is packaged within the lantern casing and powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.</p> <p>The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. Moreover, several incidents of theft and device failures were reported leading to loss of data. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to fix these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board and internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here along with the metadata.</p>
MPDD Neural Net Repository
<p>This dataset contains neural networks used within the Materials-Property-Descriptor Database (MPDD). This dataset is under construction as the MPDD is being implemented. Progress will be reported at <a href="http://phaseslab.com">phaseslab.com</a></p> <p> </p> <p>Networks available in this version:</p> <p>(From 10.5281/zenodo.4006803)</p> <p>1. SIPFENN_Krajewski_NN9</p> <p>2. SIPFENN_Krajewski_NN20</p> <p>3. SIPFENN_Krajewski_NN24<br> (end alpha_1)</p>
Repository of data supporting the thesis "Poly-algorithmic Techniques in Real Quantifier Elimination"
<p>Dataset of various files (as a .zip) supporting the PhD thesis "Poly-algorithmic Techniques in Real Quantifier Elimination" by Zak Tonks, University of Bath. The PhD thesis is in the area of Quantifier Elimination over the Reals (QE) in Computer Algebra. The PhD thesis concerns implementation of algorithms in Quantifier Elimination, which largely culminates in the package QuantifierElimination for the Computer Algebra software Maple. Much of this repository is output of the benchmarking of this package against various other packes in Maple and otherwise. Otherwise there are some auxiliary tools and files to assist with working with QE in Maple, converting between various formats, and understanding case studies and the package QuantifierElimination via software demoes as Maple worksheets.</p> <p>An overview of the contents of this repository (as a .zip file, which contains subdirectories described in the README):</p> <ul> <li>The example databases contributed from the project, as files that can be read into Maple defining tables of examples, and associated functions to build or examine various examples,</li> <li>A pdf file providing the references for all examples from the example databases, and typesetting of the examples as associated QE problems,</li> <li>The benchmarking data produced from the benchmarking of the project as csv (comma separated value) files, and the Excel workbooks (xlsx files) processing said data into survival plots for the thesis,</li> <li>Copies of the survival plots themselves as .png files,</li> <li>The bash and Maple scripts used to generate the raw benchmarking data, that can be reused, including documentation how to do so in the associated README,</li> <li>Other auxiliary tools allowing for conversion of QE formulae between formats (such as that of SyNRAC, RegularChains, QuantifierElimination (amongst packages in Maple), and QEPCAD B.</li> <li>Maple worksheets and the associated exported pdf files used in software demos at conferences to demonstrate features of QuantifierElimination.</li> <li>Some pdf files demonstrating early case studies on Lazard curtains generated from an early development build of QuantifierElimination.</li> </ul> <p>Lastly, there is a README with more detail on the files of the repository further. To emulate the benchmarking of the thesis, an understanding of bash and potentially Maple is assumed, but the raw data from the project is provided here. Before QuantifierElimination's official release, the source code and/or Maple package as an .mla file is available for interested parties upon request to the author Zak Tonks (<a href="mailto:zak.p.tonks@bath.edu">zak.p.tonks@bath.edu</a>). Any other queries about this data or associated work should be directed to this email address. The author's PhD supervisor's email address is <a href="mailto:J.H.Davenport@bath.ac.uk">J.H.Davenport@bath.ac.uk</a>.</p>
EPSRC HEED Data Repository: Surveys
<p>The HEED project aims at understanding energy needs of refugees and displaced populations to improve access to clean energy. The focus of HEED is on the lived experiences of refugees living for protracted periods of time in three refugee camps in Rwanda (Nyabiheke, Gihembe and Kigeme) and internally displaced persons (IDPs) forced to leave their homes as a result of the 2015 earthquake in Nepal. As part of the project, an energy assessment survey of households in both countries was undertaken using quantitative and qualitative research methods with households living in different parts of the camps/settlements, entrepreneurs running small businesses, and those responsible for community facilities, such as schools and health clinics. In the first phase, a questionnaire-based survey targeting displaced populations was conducted with households living in three refugee camps in Rwanda and four displaced sites in Nepal (see tables 2.1 and 2.2 respectively). The second phase of the field research involved a series of interviews and focus group discussions with various stakeholders in Nepal and Rwanda. The surveys were designed and delivered between March and April 2018 by the project partner, Practical Action. In both countries, the enumerators for the survey received a two-day training on research methods, data collection and ethics. </p> <p>With regards to the household survey, the sample size was derived using Cochran’s formula as described by Bartlett et. al. in Organizational Research: Determining Appropriate Sample Size in Survey Research. A minimum sample size of 119 households was derived by applying a margin of error of 0.03 and an alpha of 0.5. A breakdown of the focal group and specific sites where the surveys were delivered in Rwanda and Nepal is shown in tables 2.1 and 2.2 respectively. In Rwanda, a total of 814 surveys including 622 households, 155 enterprises and 37 community facilities from across three sites were conducted. The sample distribution across camp shows 211 for Gihembe, 202 for Kigeme and 209 for Nyabiheke. In Gihembe more than half of the respondents (118, 55.9%) sampled were females with the remaining 93 (44.1%) being males. This is in contrast with Kigeme where almost equal numbers of both male (100, 49.5%) and females (102, 50.5%) were sampled. In Nyabiheke the sample covered more females (123, 58.9%) than males (86, 41.1%). In Nepal, the sample covered 181 households, 18 enterprises and 3 community facilities (see table 2.2). The household sample in Nepal covered more males (126, 69.6%) than females (55, 30.4%). </p> <p><strong>Folder Structure:</strong><br> <strong>Surveys:</strong></p> <p>Gihembe Community Facility Survey – Gihembe_CF.csv<br> Gihembe Enterprise Survey – Gihembe_EN.csv<br> Gihembe Household Survey – Gihembe_HH.csv</p> <p>Kigeme Community Facility Survey – Kigeme_CF.csv<br> Kigeme Enterprise Survey – Kigeme_EN.csv<br> Kigeme Household Survey – Kigeme_HH.csv</p> <p>Nepal Community Facility Survey – Nepal_CF.csv<br> Nepal Enterprise Survey – Nepal_EN.csv<br> Nepal Household Survey – Nepal_HH.csv</p> <p>Nyabiheke Community Facility Survey - Nyabiheke_CF.csv<br> Nyabiheke Enterprise Survey – Nyabiheke_EN.csv<br> Nyabiheke Household Survey – Nyabiheke_HH.csv</p> <p><strong>Location Maps</strong>:</p> <p>Gihembe Community Facility Survey Map – CF_GIS_gihembe.csv<br> Gihembe Enterprise Survey Map – EN_GIS_gihembe.csv<br> Gihembe Household Survey Map – HH_GIS_gihembe.csv</p> <p>Kigeme Community Facility Survey Map – CF_GIS_kigeme.csv<br> Kigeme Enterprise Survey Map – EN_GIS_kigeme.csv<br> Kigeme Household Survey Map – HH_GIS_kigeme.csv</p> <p>Nepal Community Facility Survey Map – CF_GIS_nepal.csv<br> Nepal Enterprise Survey Map – EN_GIS_nepal.csv<br> Nepal Household Survey Map – HH_GIS_nepal.csv</p> <p>Nyabiheke Community Facility Survey Map - CF_GIS_nyabiheke.csv<br> Nyabiheke Enterprise Survey Map – EN_GIS_nyabiheke.csv<br> Nyabiheke Household Survey Map – HH_GIS_nyabiheke.csv</p> <p>The following information was gathered from each of the surveys:</p> <ul> <li>Households: The datasets contain information about household demographics, access to and use of electricity and lighting technologies, access to and use of cooking technologies and fuels, self-reported needs and priorities by the household, and ownership of energy products. Several key areas, such as solar lighting products and issues around fuel usage, are covered in more detail. </li> <li>Enterprises: The datasets contain information about the enterprise, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. </li> <li>Community facility: The datasets contain information about the community facility or institution, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facilities offered healthcare services were presented additional questions about specific medical devices. </li> </ul> <p>The survey results together with other methodological tools including field visits, workshops - ‘Design for Displacement (D4D)’ and ‘Energy for End-Users’ (E4E) workshops have provided relevant data and contextual knowledge to inform the design of the various interventions associated with the HEED. The data sets and results have been compiled, organised and uploaded in the data portal for use by researchers, students and all both within and outside of the project consortium, during and beyond the project lifetime.</p>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
The Online Conversation Threads Repository (Slashdot, Barrapunto, Wikipedia talk)
<p>This repository contains datasets with online conversation threads collected and analyzed by different researchers. Currently, you can find datsets from different news aggregators (Slashdot, Barrapunto) and the English Wikipedia talk pages.</p> <p>- Slashdot conversations (Aug 2005 - Aug 2006) Online conversations generated at Slashdot during a year. Posts and comments published between August 26th, 2005 and August 31th, 2006. For each discussion thread: sub-domains, title, topics and hierarchical relations between comments. For each comment: user, date, score and textual content. This dataset is different from the Slashdot Zoo social network (it is not a signed network of users) contained in the SNAP repository and represents the full version of the dataset used in the CAW 2.0 - Content Analysis for the WEB 2.0 workshop for the WWW 2009 conference that can be found in several repositories such as Konect Barrapunto conversations (Jan 2005 - Dec 2008)</p> <p>- Online conversations generated at Barrapunto (Spanish clone of Slashdot) during three years. For each discussion thread: sub-domains, title, topics and hierarchical relations between comments. For each comment: user, date, score and textual content Wikipedia (2001 - Mar 2010)</p> <p>- Data from articles discussions (talk) pages of the English Wikipedia as of March 2010. It contains comments on about 870,000 articles (i.e. all articles which had a corresponding talk page with at least one comment), in total about 9.4 million comments. The oldest comments date back to as early as 2001.</p> <p> </p>
Data of the Open Access Repository Ranking 2015
<p>The Open Access Repository Ranking 2015 ranks open access repositories from Germany, Austria and Switzerland. Data for the 2015 ranking was partly submitted by the respective repository managers, and partly automatically validated via the OAI interfaces. The OARR team reviewed all submissions assuring the quality and validity.<br> The ranking is based on an open and transparent metric that was developed in accordance with the open access community. This metric is a synthesis of different schemes and studies that surveyed and describe open access repositories.<br> Data includes the 2015 scores and descriptive information on the repositories as well as the 2015 metric.</p>
The Landscape of Research Data Repositories in 2015. A re3data Analysis
<p>The attached data sets provides an overview of the landscape of research data repositories in 2015. They are based on an analysis of the re3data - registry of research data repositories from December 2015.</p>
Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands
<p><span lang="EN-GB">This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders:<strong> 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4</strong>). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: <strong>5_Maps</strong>). All 25 LiDAR metrics were calculated using Laserfarm workflow (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: <strong>6_Masks</strong>). To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: <strong>7_Auxiliary_data</strong>). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: <strong>8_Sensitivity_analysis</strong>). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: <strong>9_Use_case</strong>). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: <strong>10_Ground_classification</strong>). </span></p> <p><span lang="EN-GB">An overview of all the folders in the repository:</span></p> <p><strong><span lang="EN-GB">1. </span></strong><strong><span lang="EN-GB">AHN1</span></strong></p> <p><strong><span lang="EN-GB">2. </span></strong><strong><span lang="EN-GB">AHN2</span></strong></p> <p><strong><span lang="EN-GB">3. </span></strong><strong><span lang="EN-GB">AHN3</span></strong></p> <p><strong><span lang="EN-GB">4. </span></strong><strong><span lang="EN-GB">AHN4</span></strong></p> <p><strong><span lang="EN-GB">5. </span></strong><strong><span lang="EN-GB">Maps</span></strong></p> <p><span lang="EN-GB">Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset.</span></p> <p><strong><span lang="EN-GB">6. Masks</span></strong></p> <ul> <li><span lang="EN-GB">ahn3_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_powerline.tif</span></li> <li><span lang="NL">ahn1_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn2_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn3_10m_NA_mask.tif</span></li> <li><span lang="NL">a</span><span lang="NL">hn4_10m_NA_mask.tif</span></li> </ul> <p><span lang="NL"> </span></p> <p><span lang="EN-GB">It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0.</span></p> <p><strong><span lang="EN-GB">7. Auxiliary data</span></strong></p> <p><span lang="EN-GB">(1) Point_density</span></p> <ul> <li><span lang="EN-GB">ahn1_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn2_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn3_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_point_density.tif</span></li> </ul> <p><span lang="EN-GB">(2) Pulse_density</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_pulse_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_pulse_density.tif</span></li> </ul> <p><span lang="EN-GB">(3) Flighttime</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_flighttime.tif</span></li> <li><span lang="EN-GB">ahn4_10m_flighttime.tif</span></li> </ul> <p><span lang="EN-GB">(4) DTM_DSM</span></p> <ul> <li><span lang="EN-GB">ahn2_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn2_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dsm.tif</span></li> </ul> <p><span lang="EN-GB">It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2</span><span lang="EN-GB">–</span><span lang="EN-GB">AHN4. All raster layers are provide at 10 m resolution.</span></p> <p><strong><span lang="EN-GB">8. Sensitivity analysis</span></strong></p> <ul> <li><span lang="EN-GB">Dunes</span></li> <li><span lang="EN-GB">Marsh</span></li> <li><span lang="EN-GB">Grassland</span></li> <li><span lang="EN-GB">Shrubland</span></li> <li><span lang="EN-GB">Woodland</span></li> <li><span lang="EN-GB">Code</span></li> <li><span lang="EN-GB">Figure</span></li> </ul> <p><span lang="EN-GB">It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis.</span></p> <p><strong><span lang="EN-GB">9. Use_case</span></strong></p> <p><span lang="EN-GB">(1) Multi-temporal_AHN</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Usecase_multi-temporal_AHN.R</span></li> </ul> <p><span lang="EN-GB">It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). </span></p> <p><span lang="EN-GB">(2) Natura2000</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Natura2000_end2021_HABITATCLASS.csv</span></li> <li><span lang="EN-GB">Natura2000_NL_habitat_grouped.csv</span></li> <li><span lang="EN-GB">Usecase_Natura2000.R</span></li> </ul> <p><span lang="EN-GB">It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. </span></p> <p><strong><span lang="EN-GB">10. Ground classification</span></strong></p> <ul> <li><span lang="EN-GB">Raw_point_cloud</span></li> <li><span lang="EN-GB">Computed_metrics </span></li> <li><span lang="EN-GB">Plottings_and_code</span></li> <li><span lang="EN-GB">ArcGIS_project</span></li> </ul> <p><span lang="EN-GB">It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project).</span></p> <p><strong><span lang="EN-GB">Code availability</span></strong></p> <p><span lang="EN-GB">Jupyter Notebooks for processing AHN datasets: </span></p> <p><span lang="EN-GB"><a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a></span></p> <p><span lang="EN-GB">Laserfarm workflow repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a></span></p> <p><span lang="EN-GB">Laserchicken software repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a></span></p> <p><span lang="EN-GB">Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a></span></p> <p><span lang="EN-GB">Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a></span></p> <p><span lang="EN-GB">Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a></span></p> <p> </p>
MassBank <=> PubChem Deposition/Annotation Repository
<p>This is a repository to exchange MassBank record and substance information to create deposition and annotation files in PubChem.</p> <p>Supporting code in: <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem/-/tree/master/massbank_eu" target="_blank" rel="noopener">https://gitlab.com/uniluxembourg/lcsb/eci/pubchem/-/tree/master/massbank_eu</a></p> <p>Credits:</p> <ul> <li>LCSB-ECI: Anjana Elapavalore, Todor Kondic, Emma Schymanski</li> <li>PubChem: Jeff Zhang, Paul Thiessen, Ben Shoemaker, Evan Bolton</li> <li>MassBank Consortium: Rene Meier, Steffen Neumann, Tobias Schulze</li> </ul> <p>Note: 20230419 files removes deprecated records from the 2022.12 release and still has InChIKeys added manually for ACES records (listed as NA) for annotation; all are present in the deposition. The unique substance file did not change, but close to 200 deprecated records were removed from the annotation set. 20230908, 20231129, 20240610, 20241126, 20250502: ACES InChIKeys added manually.</p>
Image Repository Decision Tree - Where do I deposit my imaging data
<p>Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories. </p> <p>To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories. </p> <p>Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. These versions with three trees can be used for web-view. Update: also the editable versions in powerpoint format (.pptx) are now provided. Please be aware that opening the versions with another program might lead to shifted formatting.</p> <p>Update: we now also provide ready-to-print versions designed to be printed on A3 format. One page shows the open bioimaging data tree and one page combines the preclinical and human imaging data trees. Also the editable versions of these are provided.</p>
Repository for: Patterned invagination prevents mechanical instability during gastrulation
<p>This is the repository for the paper:</p> <p>Vellutini, B. C., Cuenca, M. B., Krishna, A., Szałapak, A., Modes, C. D. & Tomancak, P. Patterned invagination prevents mechanical instability during gastrulation. <em>Nature</em> (2025). doi:<a href="https://10.1038/s41586-025-09480-3">10.1038/s41586-025-09480-3</a></p> <p>Here are all the associated repositories:</p> <ul> <li><strong>Main repository (code and data):</strong> <a href="https://doi.org/10.5281/zenodo.7781947">https://doi.org/10.5281/zenodo.7781947</a></li> <li><strong>Model and simulations (code and data):</strong> <a href="https://doi.org/10.5281/zenodo.7784906">https://doi.org/10.5281/zenodo.7784906</a></li> <li><strong>Lightsheet and in situ experiments (imaging data):</strong> <a href="https://doi.org/10.5281/zenodo.15876638">https://doi.org/10.5281/zenodo.15876638</a></li> <li><strong>Laser perturbation experiments (imaging data):</strong> <a href="https://doi.org/10.5281/zenodo.15876646">https://doi.org/10.5281/zenodo.15876646</a></li> <li><strong>Figures and videos (media files):</strong> <a href="https://doi.org/10.5281/zenodo.7781916">https://doi.org/10.5281/zenodo.7781916</a></li> </ul> <p>The main repository is maintained at <a href="https://github.com/bruvellu/cephalic-furrow">https://github.com/bruvellu/cephalic-furrow</a>.</p>
Using Repositories Workshop
<p><strong>This is the second workshop on Using Repositories in a series of workshop about Open Research Skills.</strong></p><p>This workshop covers:</p><p>Introduction to repositories</p><p>Types of repositories</p><p>How to choose the "right" repository</p><p>What are licences</p><p>Types of outputs that can be published</p><p>Demonstration</p><p>Showing how to create an account with a repository</p><p>Reserve a DOI</p><p>Archiving files</p><p>Data and metadata</p><p>Connect with other programs</p><p>Exercise </p><p>Upload files in a repository</p><p> </p><p>List of training workshops in Open Research Skills:</p><p>24th February 2023 - Open access publishing</p><p><strong>24th March 2023 - Using repositories</strong></p><p>21st April 2023 - GitHub basics</p><p>28th April 2023 - GitHub collaborative workflows</p><p>26th May 2023 - Standard vocabularies and ontologies</p><p>30th June 2023 - FAIR data</p><p><strong>Project overview:</strong></p><p>Our project aims to upskill participants in open research skills to increase the quality and reusability of phytolith research and related disciplines such as archaeology, palaeosciences and plant sciences. We will run six hands-on training workshops on open access publishing and research outputs, using repositories, ontologies and standard vocabularies, implementation of FAIR Guidelines for phytolith research, and two workshops on Github basic and advanced skills. The materials from all workshops will be archived as self-study courses on our website (<a href="https://open-phytoliths.netlify.app/">https://open-phytoliths.netlify.app/</a>). We will also provide translation during workshops and training materials into multiple languages. </p><p>Keywords</p><p>Open Access</p><p>Reproducible Research</p><p>Training</p><p>Repositories</p><p><br> </p><p>Grants: EU - Eosc-Life</p>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
CGM Scoping Review Data Repository
<p>This dataset represents the data extracted as part of a CGM-based biological feedback scoping review.</p>
A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula
<p>This data repository is a permanent archive of the results presented in the associated publication (Turner et al. 2024, Marine Ecology Progress Series, <a href="https://doi.org/10.3354/meps14567">https://doi.org/10.3354/meps14567</a>). The objective of this study was to investigate phytoplankton phenology patterns west of the Antarctic Peninsula using satellite ocean color remote sensing data. This dataset extends from 80<sup>o</sup>W to 55<sup>o</sup>W longitude and from 70<sup>o</sup>S to 60<sup>o</sup>S latitude. The data span the time period September 1997 through August 2022. This dataset includes the data and code used to create the figures in the publication. The data in this repository include chlorophyll-a concentration (Chl-a), dates of phytoplankton bloom start date and phytoplankton bloom peak date, photosynthetically active radiation (PAR), sea surface temperature (SST), wind speed, and dates of sea ice retreat and advance. Downloaded spatially-subsetted data files are included as netCDF files (extension .nc) compressed into .zip archives. Additional files used to perform the analyses and make the figures are included as MATLAB scripts and MATLAB data files (extensions .m and .mat, respectively). </p> <p>Recommended citation:</p> <p>Turner, Jessica S., (2024) A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula. Zenodo. https://doi.org/10.5281/zenodo.10790613</p>
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
A new repository of electrical resistivity tomography and ground penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard.
<p>We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (Western Svalbard, Brøggerhalvøya peninsula, Norway) as part of the project ICEtoFLUX (MUR/PRA2021 project-0027). The data set is composed of Electrical Resistivity Tomography (ERT) and GroundPenetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These examples can support the identification of the active layer and the occurrence of spatial variation of soil conditions at depth. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set is of major relevance because scant attention has been paid to the publication of geophysical data from the Ny-Ålesund area so far. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate changes studies on the Ny-Ålesund area. </p>
Questionnaire for the self-assessment of digital preservation activities in institutional research repositories
<p>This dataset includes a questionnaire designed to enable institutional repository managers to conduct a self-assessment of their digital preservation strategies and activities. It consists of 46 evaluation criteria extracted and modified from the NDSA Levels of Digital Preservation and ISO 16363:2017 standards. The questionnaire is provided in queXML format, facilitating its import into various survey applications</p> <p> </p>
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.