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Macroeconomic assessment of Climate Change Impacts
<p>Macroeconomic assessment of impacts on: Agriculture, Fishery, Forestry, Sea level rise, Riverine floods, Transport, Energy supply, Energy demand, Labour productivity, plus compounded assessment of all impacts</p>
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow
<p>An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 .</p> <p>Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with:<br> * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a)<br> * base - basal topography altitude (m)<br> * lithk - ice thickness (m)<br> * orog - surface altitude (m)<br> * strbasemag - magnitude of basal friction tb (MPa)<br> * xvelbase, yvelbase, zvelbase - 3D basal velocity (m/yr)<br> * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr)<br> * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr)<br> * n - effective pressure (MPa)</p> <p>The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the <em>As</em> Weertman sliding coeffitient.</p> <p>The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).</p>
Data for: Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)
<p>This dataset is associated to the article "Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)" published in Tectonics (<a href="https://doi.org/10.1029/2021TC006870">https://doi.org/10.1029/2021TC006870</a>).</p> <p>It includes the following:</p> <ul> <li>A description file, including a list of data files, and a description of how the marker quality score was determined in this study ("Supporting Information.docx")</li> <li>A table summarizing the historical earthquakes in the region of interest ("TableS1.xlsx")</li> <li>A table summarizing the paleoseismic investigations in the region of interest ("TableS2.xlsx")</li> <li>The full horizontal offset retrodeformations ("offsets_X.tif")</li> <li>A table of the offset values measured along the MNAF (TableS3.xlsx")</li> <li>The georeferenced fault map ("MNAF_2021.gml" and "MNAF_2021.xsd")</li> <li>A figure showing examples of vertical slip markers along the MNAF south of Iznik Lake ("FigS1.tif")</li> <li>A figure showing field examples of Late Quaternary faulting along the MNAF ("FigS2.png")</li> <li>A figure showing the results of the automatic fault discretization procedure ("FigS3.png")</li> </ul>
User Stories made by Users Workshop Analysis Results
<p>In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.</p> <p>The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>.</p> <p>The workshop has been recorded in video and the resulting data set has been published on Zenodo:</p> <p>Sell, Johann, & John, Elias. (2020). User Stories made by Users Workshop Data Set (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898358</p> <p>A qualitative analyzes has been conducted covering four iterations of coding. This data set shows the results of last iteration and the resulting insights are referenced by a research paper that is currently under review.</p> <p>We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, and to (b) investigate other interesting research questions.</p>
Trajectory Design for Proximity Operations: The Relative Orbital Elements' Perspective
<p>The data sets provided here can be used to recreate the plots of the paper “Trajectory Design for Proximity Operations: The Relative Orbital Elements’ Perspective” available at this <a href="https://arc.aiaa.org/doi/full/10.2514/1.G006175">link</a>.</p> <p>That paper presents how to rigorously transform back-and-forth the equations of the relative motion in the close-range regime between Hill-Clohessy-Wiltshire and Relative Orbital Elements formulations. As straightforward application, it is presented a methodology to generate piecewise constant acceleration profiles from an impulsive guidance solution, setting up a control grid that minimizes the difference between impulsive and equivalent delta-v burns corresponding to the acceleration profile.</p> <p>Applications are implementation of autonomous guidance and control policies for close-range satellite proximity operations.</p>
EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats
<p><strong>EUNIS-ESy</strong> is an expert system for automatic classification of European vegetation plots to habitat types of the EUNIS Habitat Classification. The EUNIS classification and the principles of the expert system are described by <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. The classification of a set of vegetation plots can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>), TURBOVEG 3 program (Hennekens 2015) and an R script (<a href="https://doi.org/10.1111/avsc.12562">Bruelheide et al. 2021</a>).</p> <p>This dataset contains two parts: (1) the expert system and related files necessary for running it; (2) characterization of EUNIS habitats based on the results of the expert system classification.</p> <p><strong>1. Expert system and related files necessary to run it</strong></p> <p>1.1. <strong>EUNIS-ESy-2025-10-03.txt </strong>– a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised EUNIS classification of vegetated Marine (MA), Coastal (N), Wetland (Q), Grassland (R), Shrubland (S), Forest (T), Inland sparsely vegetated (U) and Man-made (V). It also contains tested definitions of Aquatic plant communities (P3) and Springs (P2N). This file is different from the analogous file in the previous versions.</p> <p>1.2. <strong>Nomenclature-translation-from-Turboveg-2-databases.zip </strong>– an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (<a href="https://doi.org/10.2307/3237010">Hennekens & Schaminée 2001</a>; <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) to the nomenclature that can be used as an input for EUNIS-ESy. This file is the same as in the previous versions.</p> <p>1.3. <strong>EUNIS-ESy-User-Guide.pdf </strong>– a brief user guide to the classification of vegetation plots by EUNIS-ESy using the JUICE program. Please read this guide carefully before running the expert system to avoid misclassifications. This file is the same as in the previous versions.</p> <p><strong>2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification</strong></p> <p>2.1. <strong>EUNIS-habitats-2025-10-03.xlsx </strong>– the current list of EUNIS habitats. This file is different from the analogous file in the previous versions.</p> <p>2.2. <strong>EUNIS-EuroVegChecklist-crosswalk-2025-10-03.xlsx</strong> – a crosswalk between the EUNIS habitat classification and phytosociological alliances of EuroVegChecklist (<a href="http://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>; <a href="https://floraveg.eu/vegetation/">https://floraveg.eu/vegetation/</a>).</p> <p>2.3. <strong>EUNIS-habitats-Characteristic-species-combintation-2025-10-03.xlsx </strong>– a database of habitats' characteristic species combinations in a spreadsheet format. These species combinations are based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03. Analytical methods are described in <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. This file is different from the analogous file in the previous versions.</p> <p>2.4. <strong>EUNIS-habitats-Distribution-maps-2025-10-03.xlsx </strong>– a set of distribution maps in the TIFF format based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03.</p> <p>2.5. <strong>Data-sources-EUNIS-classification-2025-10-03.pdf </strong>– a list of data sources used to produce the distribution maps and characteristic species combinations.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p><strong>Differences from the previous version (2021-06-01)</strong></p> <p>Aquatic plant communities (P3), spring (P2N), some wetland (Q61-Q63) and some inland sparsely vegetated (U71-U72) habitats were added to the EUNIS-ESy expert system. Plant taxon concepts and nomenclature were extensively revised. Some previously included habitat definitions were slightly refined. New vegetation-plot records added to the EVA database by 8 August 2025 were used to characterize habitat types. Unlike in the previous version, this version does not provide Habitat factsheets because summarized information about each habitat is now available in the FloraVeg.EU database at <a href="https://floraveg.eu/habitat/">https://floraveg.eu/habitat/</a>.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p> </p> <p><strong>Recommended citation of this version of the EUNIS-ESy expert system</strong></p> <p>Chytrý et al. (2020), version 2025-10-03</p> <p>Chytrý M., Tichý L., Hennekens S.M., Knollová I., Janssen J.A.M., Rodwell J.S., Peterka T., Marcenò C., Landucci F., Danihelka J., Hájek M., Dengler J., Novák P., Zukal D., Jiménez-Alfaro B., Mucina L., Abdulhak S., Aćić S., Agrillo E., Attorre F., Bergmeier E., Biurrun I., Boch S., Bölöni J., Bonari G., Braslavskaya T., Bruelheide H., Campos J.A., Čarni A., Casella L., Ćuk M., Ćušterevska R., De Bie E., Delbosc P., Demina O., Didukh Y., Dítě D., Dziuba T., Ewald J., Gavilán R.G., Gégout J.-C., Giusso del Galdo G.P., Golub V., Goncharova N., Goral F., Graf U., Indreica A., Isermann M., Jandt U., Jansen F., Jansen J., Jašková A., Jiroušek M., Kącki Z., Kalníková V., Kavgacı A., Khanina L., Korolyuk A.Yu., Kozhevnikova M., Kuzemko A., Küzmič F., Kuznetsov O.L., Laiviņš M., Lavrinenko I., Lavrinenko O., Lebedeva M., Lososová Z., Lysenko T., Maciejewski L., Mardari C., Marinšek A., Napreenko M.G., Onyshchenko V., Pérez-Haase A., Pielech R., Prokhorov V., Rašomavičius V., Rodríguez Rojo M.P., Rūsiņa S., Schrautzer J., Šibík J., Šilc U., Škvorc Ž., Smagin V.A., Stančić Z., Stanisci A., Tikhonova E., Tonteri T., Uogintas D., Valachovič M., Vassilev K., Vynokurov D., Willner W., Yamalov S., Evans D., Palitzsch Lund M., Spyropoulou R., Tryfon E., Schaminée J.H.J. (2020) EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/avsc.12519</p>
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
BIP! DB: A Dataset of Impact Measures for Research Products
<h2>Overview</h2> <p>This dataset contains citation-based impact indicators (also referred as <em>measures</em>) for ~296M distinct persistent identifiers (PIDs) that correspond to various types of research products (publications, datasets, software, and other products).</p> <p>The calculated indicators are organized into categories based on the aspect of impact they capture. </p> <h3>Influence indicators</h3> <p>Reflect the "total" impact of a research product; how established it is in general.</p> <ul> <li><strong><em>Citation Count:</em></strong> The total number of citations of the product, the most well-known influence indicator.</li> <li><strong><em>PageRank score:</em> </strong>An influence indicator based on the PageRank (Page et al., 1999), a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score). </li> </ul> <h3>Popularity indicators</h3> <p>Capture the "current" impact of a research product; how popular it currently is.</p> <ul> <li><strong><em>RAM score:</em></strong> A popularity indicator based on the RAM (Ghosh et al., 2011) method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).</li> <li><strong><em>AttRank score:</em></strong><strong> </strong>A popularity indicator based on the AttRank (Kanellos et al., 2020) method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.</li> </ul> <h3>Impulse indicators</h3> <p>Measure the initial momentum that a research product received right after its publication.</p> <ul> <li><em><strong>Incubation Citation Count (3-year CC):</strong> </em>This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.</li> </ul> <h3>FIeld-weighted indicators</h3> <p>Capture the impact of a research product relative to the average performance in its field, accounting for differences in citation practices across disciplines.</p> <ul> <li><strong>Field-Weighted Citation Impact (FWCI):</strong> A field-weighted indicator that measures how a research product performs compared to the global average in its research field. An FWCI of 1.0 indicates that the product is cited exactly as expected for similar publications in the same field; values above 1.0 indicate above-average impact, while values below 1.0 indicate below-average impact.</li> <li><strong>3-year FWCI:</strong> A time-restricted version of the FWCI that considers citations received within the first three years after publication. By limiting the citation window, this indicator captures the early relative impact of a research product, providing insight into how quickly it gains influence in its field.</li> </ul> <p>In our analysis, the expected number of citations for each research product is computed by <em>grouping them by concept, publication year, and product type and then averaging the citations within each group</em>. </p> <p><em>More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found <a href="https://bip.imsi.athenarc.gr/site/indicators">here</a> and in the respective references (Kanellos et al., 2019).</em></p> <h2>Indicator calculation levels</h2> <p>The impact indicators are calculated in two levels:</p> <ul> <li><strong>PID level: </strong> assuming that each PID corresponds to a distinct research product. Currently PIDs are DOIs, PMCIDs, and PMIDs.</li> <li><strong>OpenAIRE-id level: </strong>leveraging PID synonyms based on OpenAIRE's deduplication algorithm (Manghi et al., 2020) - each distinct article has its own OpenAIRE id.</li> </ul> <h2>Impact classes</h2> <p>Each researcj product is also assigned an impact class, reflecting its percentile rank among all products in the dataset: </p> <table style="border-collapse: collapse; width: 100%; height: 39.1876px;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>Class</strong></td> <td style="height: 19.5938px;"><strong>Percentile</strong></td> <td style="height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">C1</td> <td style="height: 19.5938px;">Top 0.01%</td> <td style="height: 19.5938px;">Exceptional impact</td> </tr> <tr> <td>C2</td> <td>Top 0.1%</td> <td>Very high impact</td> </tr> <tr> <td>C3</td> <td>Top 1%</td> <td>High impact</td> </tr> <tr> <td>C4</td> <td>Top 10%</td> <td>Good impact</td> </tr> <tr> <td>C5</td> <td>Rest 90%</td> <td>Remaining products</td> </tr> </tbody> </table> <h2>File structure</h2> <p>For each calculation level (PID / OpenAIRE-id) we provide five (5) compressed CSV files (one for each measure/score provided). The structure of the files differs slightly depending on the level:</p> <ul> <li> <p><strong>PID-level files:</strong> Each line follows the format:<br><code>identifier <tab> identifier_type <tab> score <tab> class</code></p> </li> <li> <p><strong>OpenAIRE-id-level files:</strong> These files contain the keyword "openaire_ids" in the filename. Each line follows the format:<br><code>identifier <tab> score <tab> class</code></p> </li> </ul> <p><em>The parameter setting of each measure is encoded in the corresponding filename. For more details on the different measures/scores see our extensive experimental study (Kanellos et al., 2019) and the configuration of AttRank in the original paper (Kanellos et al., 2020).</em></p> <h3>Topic-related files</h3> <p>In addition to the main indicator files, the dataset also includes <em>topic-level outputs</em>, providing <em>field-weighted impact indicators</em> as well <em>percentile classes</em> within the associated <em>2nd-level concepts from OpenAlex</em>. </p> <p>Specifically, we associated all research products with their 2nd level concepts from OpenAlex (using only their <em>DOIs</em>); we kept only the three most dominant concepts for each product, based on their confidence score, and only if this score was greater than 0.3.</p> <p>Since currently only the DOIs are used to associate concepts from OpenAlex to research products, all identifiers in these files refer to DOIs. </p> <ul> <li><strong>Topic-specific impact classes file:</strong> Fore each concept and indicator, precentile classes are computed and provided in <code>topic_based_impact_classes.txt</code> in the following format:</li> </ul> <p><code>identifier <tab> concept <tab> pagerank_class <tab> attrank_class <tab> 3-cc_class <tab> cc_class</code></p> <ul> <li><strong>Field-weighted indicator files:</strong> Each line follows the format:<br><code>identifier <tab> concept <tab> score</code></li> </ul> <p><em>Note that to prevent division by zero, the score column is left empty whenever the average score for a specific combination of concept, publication year, and product type equals zero.</em></p> <h2>Data sources</h2> <p>The data used to produce the citation network on which we calculated the provided measures have been gathered from the OpenAIRE Graph v10.5.0, including data from (a) <em>OpenCitations' COCI & POCI dataset</em>, (b) <em>MAG</em> (Sinha et al, 2015; Wang et al., 2019), and (c) <em>Crossref</em>. The union of all distinct citations that could be found in these sources have been considered. </p> <p>Additionally, all topic-related computations are derived from OpenAlex concepts.</p> <h2>Access and Use</h2> <p>Find our Academic Search Engine built on top of these data <a href="https://bip.imsi.athenarc.gr/">here</a>. Further note, that we also provide all calculated scores through <a href="https://bip-api.imsi.athenarc.gr/documentation">BIP! Finder's API</a>. </p> <p><em>Terms:</em> These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license.</p> <h2>Changelog</h2> <p><strong>v19.1</strong></p> <ul> <li>[major update] Added field-weighted indicators: FWCI and 3-year FWCI.</li> </ul> <p><strong>v19.0</strong></p> <ul> <li>Added PMCID as an additional type of PID.</li> </ul> <p><strong>v15.1</strong></p> <ul> <li>Fixed missing records that were unintentionally omitted in v15.0</li> <li>Ensures all popularity indicators correctly use <code>current_year = 2025</code></li> </ul> <p><strong>v12.0</strong></p> <ul> <li>Added PMIDs as an additional type of PID.</li> </ul> <p><strong>v10.0</strong></p> <ul> <li>[Major update] Introduced deduplication of research products using the latest <a href="https://graph.openaire.eu/docs/graph-production-workflow/deduplication/research-products">OpenAIRE article deduplication algorithm</a>. Each node in the citation network is now a deduplicated product having a distinct OpenAIRE id. <ul> <li>Corrected overcounting of citations caused by multiple versions of the same product.</li> <li>PID-level scores are now derived from deduplicated OpenAIRE nodes.</li> </ul> </li> <li>Added filtering rules described <a href="https://graph.openaire.eu/docs/graph-production-workflow/aggregation/non-compatible-sources/doiboost/#crossref-filtering">here</a> to remove from dataset PIDs with problematic metadata. </li> </ul> <p><strong>v9.0</strong></p> <ul> <li>[Major update] Introduced topic-specific impact classes for PID-identified products based on OpenAlex 2nd-level concepts.</li> </ul> <p><strong>v7.0</strong></p> <ul> <li>[Major update] Added impact class labels (C1-C5) for each procuct, indicating the percentile-bsaed impact levels. <ul> <li>Classes reflect relative position within the global score distribution.</li> </ul> </li> </ul> <p><strong>v5.1</strong></p> <ul> <li>[Major update] Introduced dual-level score computation: PID level and OpenAIRE ID level.</li> </ul>
Atomic force microscopy indentation data of zebrafish spinal cord sections
<p>The HDF5 file was created using the Python package nanite. It contains 1132 raw atomic force microscopy (AFM) force-indentation curves of zebrafish spinal cord sections, the preprocessed curves, and the corresponding fits to the approach part. In addition, a manual rating was assigned to each force-indentation curve. The intended use of this dataset is the application of machine-learning approaches to quantify AFM data quality for biological tissues.</p>
SMOS Brightness Temperatures at 40° incidence angle Arctic
<p>This is a data set of polarised brightness temperatures (TBs) at 40° incidence angle from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture and Ocean Salinity (SMOS) mission. The data set was produced to enable a consistent combination of TBs from SMOS with those measured by the SMAP (Soil Moisture Active Passive) satellite.</p> <p>It is based on the version v620 SMOS L1C brightness temperatures, which are not corrected with respect to solar and cosmic radiation or atmospheric effects. A fitting function is applied to the daily multi-angular SMOS data (see Zhao et al., 2015 and Schmitt and Kaleschke, 2018) to obtain brightness temperature values at the SMAP incidence angle of 40°. The data are gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMOS and SMAP data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMAP TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation at 40° incidence angle<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation at 40° incidence angle<br> <strong>RMSE_v</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>RMSE_h</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>nmp</strong> - number of incidence angles used for the fit<br> <strong>dataloss</strong> - fraction of discarded data</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin
<p>Animations of the data are available here: <a href="https://doi.org/10.5281/zenodo.2438110">https://doi.org/10.5281/zenodo.2438110</a></p> <p>If you are using this data set, please cite the following publication:</p> <p>Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 </p> <p>The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157</p> <p>URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p> <p>Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. </p> <p>Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). </p> <p>The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean. </p>
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
Malawi probabilistic seismic hazard analysis (PSHA) using the Malawi Seismogenic Source Model (MSSM). Supplementary Files v1.1
<p>Updated (October 2022) version of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi. The PSHA codes themselves (v1.0) are available at: https://doi.org/10.5281/zenodo.7265781and the most recent version will be available on GitHub at: https://github.com/jack-williams1/Malawi_PSHA. Note the variables stored here are not stored on GitHub due to the file size.</p> <p>Includes both input files for performing PSHA and output ground motions for plotting PSHA results.</p> <p>Files are:</p> <ul> <li>malawi_Vs30_active.txt: Input USGS slope-based Vs30 values for Malawi (Wald and Allen 2007)</li> <li>EQCAT_comb.mat: MSSM Direct catalog for all possible rupture weightings (stored as MATLAB variable)</li> <li>GM_MSSM_em_20221027: Ground motions for plotting PSHA maps (stored as MATLAB variable)</li> <li>GM_MSSM_20221021.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable)</li> <li>mssm_comb.mat: Matlab file for combined MSSM Direct and Adapted MSSM catalogs (stored as MATLAB variable)</li> <li>MSSM_Catalog_Adapted_em.mat: Adapated MSSM event catalog (stored as MATLAB variable)</li> <li>syncat_bg.mat: Areal source stochastic event catalog (stored as MATLAB variable)</li> </ul> <p>Further descriptions of these files and how to use them are provided on Github. An open-access manuscript describing the PSHA is available at: </p> <p>Williams J. N., Werner M. J., Goda K., Wedmore L. N. J., De Risi R., Biggs J., Mdala H., Dulanya Z., Fagereng Å, Mphepo F., Chindandali P. (2023). Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi, Geophysical Journal International, Volume 233, Issue 3, June 2023, Pages 2172–2206, <a href="https://doi.org/10.1093/gji/ggad060">https://doi.org/10.1093/gji/ggad060</a></p> <p>Please reference this publication along with this repository when using these data.</p> <p>USGS vs30 value compilation described in:</p> <p>Allen, T. I., and Wald, D. J., 2009, On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30), Bulletin of the Seismological Society of America, 99, no. 2A, 935-943.</p> <p> </p>
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
WikiPathways Nanomaterials Portal
<p>Archive of the WikiPathways Nanomaterials Portal on 8 November 2022. Includes PNG images and original GPML files.</p>
Dataset for "Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity."
<p>This repository contains the tropical Pacific sea surface temperature and global precipitation data from the CESM1 time slice experiments, which were used for the analysis presented in Karamperidou & DiNezio (2022), Nature Communications (https://www.nature.com/articles/s41467-022-34880-8)</p> <p> </p> <p>From Karamperidou & DiNezio (2022):</p> <p>“To assess the response of ENSO flavors to orbital forcing over the past 12,000 years (12ka), we use a suite of time-slice experiments in 3ka intervals with version 1 of the Community Earth System Model (CESM1). Each experiment is 400-600 years long and was run until the surface climate and oceanic processes controlling tropical climate, such as the depth of the thermocline in the equatorial Pacific or the Atlantic Meridional Overturning Circulation (AMOC), have reached equilibrium. All simulations exhibit minimal drift in global mean surface temperature (less than 0.05<sup>o</sup>C per century), tropical mean surface temperature (less than 0.04<sup>o</sup>C per century), the depth of the equatorial thermocline in the Pacific (less than 0.3m per century), and the strength of the AMOC (less than 0.25 Sv per century) during the periods used in the analyses. With the exception of the 12 ka BP interval which includes ice sheet changes and lower greenhouse gases, the primary forcing in the 0, 3, 6, and 9 ka BP intervals is changes in Earth's precession, and each simulation branched off its preceding one, starting from 0ka sequentially through the Holocene. The maximum TOA energetic imbalance does not exceed 0.45 Wm<sup>-2</sup>, which is much smaller than the imposed radiative forcing.”</p> <p> </p> <p> </p> <p>Karamperidou, C., DiNezio, P.N. Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. <em>Nat Commun</em> <strong>13</strong>, 7244 (2022). https://doi.org/10.1038/s41467-022-34880-8</p>
MesoLF demo data and auxiliary files
<p>Demo data and auxiliary files accompanying the article:</p> <p>Nöbauer, T., Zhang, Y., Kim, H. & Vaziri, A.<br> Mesoscale volumetric light-field (MesoLF) imaging of neuroactivity across cortical areas at 18 Hz.<br> <em>Nature Methods</em> 1–10 (2023). doi:<a href="https://doi.org/10.1038/s41592-023-01789-z">10.1038/s41592-023-01789-z</a><br> <br> The files provided here are required for running a demo of the MesoLF pipeline. These files will be downloaded automatically by the Matlab live notebook "mesolf_demo.mlx" that was published as part of "Supplementary Software 1" with the associated article. For installation instructions, see file "README.md" in "Supplementary Software 1". For future software updates, check <a href="https://github.com/vazirilab">https://github.com/vazirilab</a></p>
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt)
<p>This dataset contains quantitative student data acquired during the administration of two validated Computational Thinking (CT) assessments for upper primary school (grades 3 and 4): the Beginners' CT test (BCTt) [1] and the comptent CT test (cCTt) [2]</p> <p>To compare the psychometric properties of both instruments a comparative analysis was conducted with data acquired in schools in Portugal from the same school districts. More specifically, we analyse the results of: </p> <p>- the BCTt test administered in March 2020 to 374 students in grades 3-4,</p> <p>- the cCTt test administered in April 2021 to 201 different students in grades 3-4.</p> <p>These students had no prior experience in Computational Thinking, as this was not part of the national curriculum at the times of administration. </p> <p> </p> <p>The detailed psychometric comparison is published in Frontiers in Psychology - Educational Psychology [3] and provides indications regarding the use of both instruments for grades 3-4. </p> <p> </p> <p>A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the specific content of the 2 csv files</p> <p> </p> <p>The BCTt is available upon request to maria.zapata@urjc.es and the cCTt items are available in [2] with an editable version being available upon request to laila.elhamamsy@epfl.ch. </p> <p>In case of other inquiries, please contact: laila.elhamamsy@epfl.ch, maria.zapata@urjc.es or pedro.marcelino@treetree2.org</p> <p> </p> <p><strong>References</strong></p> <p>[1] M. Zapata-Cáceres, E. Martín-Barroso and M. Román-González, "Computational Thinking Test for Beginners: Design and Content Validation," <em>2020 IEEE Global Engineering Education Conference (EDUCON)</em>, 2020, pp. 1905-1914, doi: 10.1109/EDUCON45650.2020.9125368.</p> <p>[2] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. <em>Journal of Educational Computing Research</em>, <em>60</em>(7), 1818–1866. <a href="https://doi.org/10.1177/07356331221081753">https://doi.org/10.1177/07356331221081753</a></p> <p>[3] <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=LailaEl-Hamamsy&UID=781667">Laila El-Hamamsy</a>* , <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=Mar%C3%ADaZapata-C%C3%A1ceres&UID=2073859">María Zapata-Cáceres</a>, Pedro Marcelino, Jessica Dehler Zufferey, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=BarbaraBruno&UID=893934">Barbara Bruno</a>, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=EstefaniaMart%C3%ADn&UID=2086979">Estefanía Martín-Barroso</a> and <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=MarcosRom%C3%A1n-Gonz%C3%A1lez&UID=760761">Marcos Román-González</a> (2022). <a href="http://www.frontiersin.org/Journal/Abstract.aspx?d=0&name=Educational_Psychology&ART_DOI=10.3389/fpsyg.2022.1082659">Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: the Beginners' CT test (BCTt) and the competent CT test (cCTt)</a>. <em>Front. Psychol.</em> doi:10.3389/fpsyg.2022.1082659</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.