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Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Evaluating rose yield responses to compost treatments: Data from an 18-month field study in Kenya
<p>This dataset and these scripts supports the manuscript 'Modelling cut rose yield after compost amendment over an 18-month period using repeated sigmoidal Gompertz curve fitting' by Evy de Nijs, Roland Bol, Albert Tietema & Emiel van Loon. </p> <p>Roses are an important crop for the floricultural sector of Kenya. Roses are a perennial crop and under continuous production for six to ten years. To optimize rose production, it is essential to understand how different management practices impact yield over time. This dataset contains a detailed record of rose yield data collected in an 18-month large-scale field experiment. The aim of this experiment was to evaluate the effect of pre-planting compost amendment on the yield and quality of cut roses. It was conducted in a polythene greenhouse near lake Naivasha, Kenya. Yield data included the number of stems harvested per day per flowering bed. Data presented here offer a comprehensive view of the impacts of different compost treatments on the yield of cut roses. Combined with the offered scripts, this is the framework presented in the aforementioned manuscript. This approach allows to use repeated growth curves to analyze yields compared to a baseline General Additive Model. </p> <p> </p> <p>de Nijs, E. A., Tietema, A., Bol, R., & van Loon, E. E. (2025). Modeling Cut Rose Yield Over an 18‐Month Period After Compost Amendment Using Repeated Sigmoidal Gompertz Curve Fitting. <em>Plant‐Environment Interactions</em>, <em>6</em>(3), e70049.</p>
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
S38 | SOLNSLMCTPS | SOLUTIONS Predicted Transformation Products by LMC
<p>This is the collection associated with list S38 SOLNSLMCTPS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S38 | SOLNSLMCTPS | <strong>SOLUTIONS Predicted Transformation Products by LMC</strong></p> <p>Predicted Transformation Products calculated by LMC during the SOLUTIONS project, interactive table available <a href="https://www.normandata.eu/solutions/modelsTransformationProducts.php">here</a>.</p> <p>14/11/19 update: added CSV version. 9/7/2025: fixed several corrupt SMILES and added InChIKeys to XLSX/CSV. Note that the author had to be changed to the University to satisfy Zenodo upload requirements, the original authors were listed as <a href="https://oasis-lmc.org/about/contacts.aspx">LMC</a>. </p>
HiLSS Project
<p><em>This repository is periodically updated</em>.</p> <p><a href="https://cordis.europa.eu/project/id/890561"><strong>Historic Landscape and Soil Sustainability (MSCA-IF-2019 - Individual Fellowships)</strong></a></p> <p>The HiLSS Project aims to investigate the relationships between sustainability and landscape heritage with particular reference to soil loss and degradation over the long term. The project will take a multidisciplinary approach that combines archaeology, Historical Landscape Characterisation (HLC), geosciences, and computer-based geospatial analysis (GIS - Geographical Information Systems) and modelling (RUSLE - Revisited Universal Soil Loss Equation). The research objectives of the HiLSS project are to quantify the impact of human activities during the Late Holocene in order to create spatial models which can inform the development of sustainable conservation strategies for rural landscape heritage.<br>This project will focus on two mountainous regions that present historical and cultural similarities but located in different climatic zones of Europe (1- Tuscan-Emilian Apennines, Italy; 2- Northern-mid Galicia, Spain). In previous HLC studies, land-use has been evaluated from the perspective of cultural heritage, whereas RUSLE have used it as a proxy for the land-cover of an area and its effect on soil erosion. The HiLSS project will propose an innovative methodology that combines both the historic/cultural values and the environmental values of land-use to inform development of a model for the sustainable conservation. By considering the different agricultural land-use HLC types in GIS-RUSLE modelling, it will be possible to quantify the effect on soil loss for each HLC type and consequently to devise more environmentally sustainable management for each type.<br>Environmental sustainability and historic landscape conservation are typically treated as two separate fields, but the HiLSS project will develop a transformative model for interdisciplinary research, proposing a new way to embrace both cultural and natural values as components of the same landscape management plans.</p> <blockquote> <p><strong>HLC_RUSLE.zip</strong></p> </blockquote> <p>The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: "<a href="https://doi.org/10.1038/s41598-023-31334-z"><em>Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. - Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)</em></a>".</p> <p>List of files included in <em>HLC_RUSLE.zip</em>:</p> <ul> <li><em>R_script_code named "HLC_RUSLE" in .rmd format</em></li> <li><em>Output folder: </em> <ul> <li><em>Figures folder: .png products of the R script code</em></li> <li><em>Rasters folder: .png products of the R script code</em></li> <li><em>Tables folder: .pdf products of the R script code</em></li> </ul> </li> <li><em>GeoTiff folder (.TIFF file format): Regional RUSLE Data</em></li> <li><em>GPKG:</em> <em>HLC </em>dataset and <em>Region Of Interest file in .gpkg format</em></li> </ul> <blockquote> <p><strong>Spatial statistics to reveal patterns and connections in the historic landscape</strong></p> </blockquote> <p>The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: " <a href="https://www.tandfonline.com/doi/full/10.1080/17445647.2022.2088305"><em>F. Brandolini & S. Turner (2022) Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy), Journal of Maps, DOI: 10.1080/17445647.2022.2088305.</em></a> ".</p> <p>It is available at:<a href="http:// doi.org/10.5281/zenodo.5907229"> </a><a href="http://doi.org/10.5281/zenodo.5907229">https://doi.org/10.5281/zenodo.5907229</a></p> <blockquote> <p><strong>Supplementary material_Land _SI_Historic Landscape Evolution.zip</strong></p> </blockquote> <p>Supplementary Materials to accompaing the paper: <em>The evolution of historic agroforestry landscape in the Northern Apennines (Italy) and its consequences for slope geomorphic processes</em>, submitted to <em>Land, </em>Special Issue <em>Historic Landscape Transformation.</em></p> <blockquote> <p><strong>Project_Publications.zip</strong></p> </blockquote> <p>List of .pdf file included in the folder: </p> <p>1) Brandolini F, Domingo-Ribas G, Zerboni A and Turner S. A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features [version 2; peer review: 2 approved, 1 approved with reservations]. Open Res Europe 2021, <strong>1</strong>:22 (<a href="https://doi.org/10.12688/openreseurope.13135.2">https://doi.org/10.12688/openreseurope.13135.2</a>)</p> <p>2) Brandolini F., Turner S. 2022 - Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy), Journal of Maps, (<a href="https://doi.org/10.1080/17445647.2022.2088305">https://doi.org/10.1080/17445647.2022.2088305</a>)</p> <p>3) Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. 2023 - Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023), (<a href="https://doi.org/10.1038/s41598-023-31334-z">https://doi.org/10.1038/s41598-023-31334-z</a>)</p> <p>4) Brandolini, F., Compostella, C., Pelfini, M., and Turner, S. 2023 - "The Evolution of Historic Agroforestry Landscape in the Northern Apennines (Italy) and Its Consequences for Slope Geomorphic Processes" Land 12, no. 5: 1054. (<a href="https://doi.org/10.3390/land12051054">https://doi.org/10.3390/land12051054</a>)</p> <p>5) Sánchez-Pardo, José Carlos, et al. "Dating and Characterising the Transformation of a Monastic Landscape: A Multidisciplinary Approach to the Agrarian Spaces of Samos Abbey (NW Spain)." Environmental Archaeology, published online March 11, 2024. (<a href="https://doi.org/10.1080/14614103.2024.2319954">https://doi.org/10.1080/14614103.2024.2319954</a>)</p> <p>6) Kinnaird, Tim C., et al. - "Unearthing the Histories of Agrarian Landscapes: A Research Framework for Terraces as Sustainable Environments." Geoarchaeology 40, no. 2 (2025): e70004. (<a href="https://doi.org/10.1002/gea.70004">https://doi.org/10.1002/gea.70004</a>)</p> <p>7) Brandolini, F. et al. - "Geoarchaeology reveals development of terrace farming in the Northern Apennines during the Medieval Climate Anomaly". Sci Rep 15, 24989 (2025). (<a href="https://doi.org/10.1038/s41598-025-08396-2">https://doi.org/10.1038/s41598-025-08396-2)</a></p> <p> </p>
Data to 'The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0'
<p>This dataset includes the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0" (<a href="https://www.doi.org/10.1111/jiec.70023" target="_blank" rel="noopener">DOI: 10.1111/jiec.70023</a>). When using the dataset in your own work, please cite the article and provide reference to this zenodo repository.</p> <p>For importing the country-level characterization factors into LCA software, please see the AWARE2.0 implementations (openLCA, SimaPro, brightway2) in IMPACT World+, version 2.1: <a title="IMPACT World+ version 2.1" href="https://doi.org/10.5281/zenodo.14041258">https://doi.org/10.5281/zenodo.14041258</a></p> <h3>Content</h3> <p><strong>- native resolution (monthly, watershed scale):</strong></p> <ul> <li><strong>AWARE20_Native_CFs_geospatial.gpkg</strong>: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly and annual CFs</li> <li><strong>AWARE20_Native_CFs_geospatial.kmz</strong>: Version of <em>AWARE20_Native_CFs_geospatial.gpkg </em>for GoogleEarth</li> <li><strong>AWARE20_Native_CFs.xlsx</strong>: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting</li> <li><strong>AWARE20_Intermediate_Variables.xlsx</strong>: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc.</li> <li><strong>figures_AWARE_AWARE20_comparison_all_basins.zip</strong>: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0.</li> </ul> <p><strong>- spatiotemporal aggregations:</strong></p> <ul> <li><strong>AWARE20_Countries_and_Regions.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10) </a></li> <li><strong>AWARE20_Subnational_Resolution.xlsx</strong>: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (<a href="https://gadm.org/old_versions.html" target="_blank" rel="noopener">https://gadm.org/old_versions.html</a>)</li> <li><strong>AWARE20_Crop_Specific.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10)</a>, using crop-specific irrigation water consumption for 27 crop classes as spatiotemporal weights. See readme sheet in Excel file for more information.</li> </ul> <p> </p> <h3><strong>Changes:</strong></h3> <ul> <li>v1.0.1: <ul> <li>addition of crop-specific spatiotemporal aggregations (AWARE20_Crop_Specific.xlsx)</li> </ul> </li> <li>v1.0.0 (corresponding to published article): <ul> <li>update of readme sheets with appropriate references to corresponding article</li> <li>update of reference "Müller Schmied et al. (2024)"</li> <li>added file: AWARE20_Subnational_Resolution.xlsx</li> </ul> </li> <li> v0.0.3: <ul> <li>use bug-fixed WaterGAP2.2e data from Sept 2023</li> <li>added country and subnational aggregations</li> <li>changed "NoData" to "NotDefined" in the tables</li> <li>added gridcell pHWC to intermediate variables</li> <li>corrected table of water consumption data without post-processing in "Intermediate_Variables"</li> </ul> </li> </ul> <p> </p> <h3><strong>Caveats:</strong></h3> <ul> <li>Spatial CF aggregations for treaties: <ul> <li>Due to the creation date of the data set, the <strong>BRICS aggregations </strong>in<strong> </strong><em>AWARE20_Countries_and_Regions.xlsx</em> do not include the states that joined after 2023. In <em>AWARE20_Crop_Specific.xlsx</em>, the 10-member BRICS is labeled BRICS+.</li> </ul> </li> </ul>
Ethnic and Migrant Minorities (EMM) Survey Registry: All metadata records
<p>The <a href="https://ethmigsurveydatahub.eu/emmregistry/">Ethnic and Migrant Minorities (EMM) Survey Registry</a> is a free online tool that allows users to search for and learn about existing quantitative surveys undertaken with EMM (sub)populations conducted in 34 European countries, from 2000 onwards, through compiled survey-level metadata.</p> <p>The first version was produced by a team led by CEE (Sciences Po, CNRS) and jointly funded through the COST Action 16111 – ETHMIGSURVEYDATA (a network of more than 200 European researchers active in the ethnic and migration studies field), the Horizon 2020 infrastructure project SSHOC (within Task 9.2 on Ethnic and Migration Studies, within Work Package 9 on Data Communities) and the project FAIRETHMIGQUANT (an Open Science project funded by the French Agence Nationale de la Recherche, ANR).</p> <p>This specific record includes the metadata for 2,120 survey records as .dta, .sav and .csv files published on the Registry, as of 31.07.2025.</p>
NOAFAULTS KMZ layer Version 7.0
<p>The NOAFAULTs database of active faults of Greece was first published in 2013 at BGSG (versions 1.0 & 1.1; <a href="http://dx.doi.org/10.12681/bgsg.11079"><em>http://dx.doi.org/10.12681/bgsg.11079</em></a>). Version 2.1 (see map below) was published in 2018 <a href="http://doi.org/10.5281/zenodo.3483136"><em>http://doi.org/10.5281/zenodo.3483136</em></a>); Version 3.0 was published in 2020 <a href="http://doi.org/10.5281/zenodo.4304613"><em>http://doi.org/10.5281/zenodo.4304613</em></a><em>;</em> Version 4.0 was published in 2022 <a href="https://zenodo.org/record/6326260"><em>https://zenodo.org/record/6326260</em></a> ; Version 5.0 was published in 2023 <a href="https://doi.org/10.5281/zenodo.8075517"><em>https://doi.org/10.5281/zenodo.8075517</em></a> ; Version 6.0 was published in 2024 <a href="https://zenodo.org/records/13168947"><em>https://zenodo.org/records/13168947</em></a> . NOAFAULTs was created towards compiling a digital database of fault traces, geometry and additional attributes (kinematics, slip rate, associated seismicity etc.) primarily to support seismicity monitoring at the National Observatory of Athens (NOA). It has been constructed from published fault maps in peer-reviewed journals since 1972 while the number of the scientific papers that have contributed with fault data in version 7.0 is <strong>158</strong>. The standard commercial software ARCGIS has been used to design and populate the database. The fault layer was produced at NOA by on-screen digitization of fault traces at the original map-scale (as drawn by the reference paper it was taken from) and is available through our web portal application <a href="https://arcg.is/04Haer">https://arcg.is/04Haer</a> supported by ESRI.</p> <p>In this version, in order to streamline the process, avoid inconsistencies during data input, and ensure a homogeneous database, we decided to automatically calculate certain fields from the attribute table. Specifically, the <em>Strike</em> and <em>Dip-Direction</em> fields were derived programmatically. First, the digitization of the faults was carried out to align with the dip-direction of each. With this method of digitization, the user also could apply a symbol to each fault that correctly corresponds to its dip-direction, enhancing the accuracy and interpretability of the fault representation. Then, by calculating the line bearing of the fault and applying a ±180° function to the result, the <em>Strike</em> was determined. Subsequently, using the calculated strike and appropriate functions, the <em>Dip-Direction</em> of each fault was generated. If the dip angle was not provided by the scientific source, we assigned standard values: 60° for Normal faults, 30° for Reverse faults, and 90° for Strike-Slip faults. Consequently, the Rake field was assigned values of -90° for Normal, 90° for Reverse, and 0° or 180° for Strike-Slip faults, depending on relative sense of motion.</p> <p><u>Four new thematic layers were added to V7.0: </u>the Sampling Sites layer, which includes locations from paleoseismological trenches and <sup>36</sup>CL sampling sites; the NOA Surface Rupture Database layer, which contains documented surface ruptures from field mapping ; and the Cyprus Faults layer, provided by the Cyprus Geological Survey Department (GSD<a title="" href="#_ftn1" name="_ftnref1">[1]</a>) , incorporated after initial evaluation and selection of specific features. Furthermore, the Focal Mechanisms layer from the NOA Gisola Catalogue (2012–2025) was updated to include data up to <em>3 June 2025</em>, while the Strong Earthquakes in Greece since 1964 layer was updated with events up to <em>2 June 2025</em>. In addition, one more station was added to the RING GNSS Network (INGV) layer, enhancing the coverage and detail of the database Moreover, the Rupture Plane of the 2020 Samos Earthquake (M7.0) was added.</p> <div> <p>In this version a total number of <strong>3441</strong> active faults are included. 91.8% of the active faults are normal faults, 5.4% are strike-slip faults and only 2.8% represent the reverse faults. 390 new fault traces were added in South Gulf of Evia, NW Crete, Karpathos Island, North Peloponnese, Amorgos basin (fault codes range from GR3436 to GR3825). Also, reliable data on slip rates is available for <strong>164</strong> faults. Limited data on instrumental and historical seismicity are linked to 173 and 121 active faults, respectively. In addition, a) surface-rupturing geological data and b) data on the proximity of epicentres of strong seismic events to the traces of active faults allows the identification of <strong>109</strong> rupturing faults (seismic faults) that included in this version of the database. The NOAFAULTs database shows that nearly 51% of its active faults imply high seismic risk level in the broader area of Greece. These active faults can generate surface faulting or strong ground motions that can cause serious damage to buildings and infrastructures and therefore represent a significant hazard, particularly in the densely populated and industrialized areas of Greece.</p> <br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> <em>https://www.moa.gov.cy/moa/gsd/gsd.nsf/All/870AEC05A4324A31C22585F9003EC70B?OpenDocument</em></p> </div> </div> <p> </p> <p>Since 2022 NOAFAULTs has contributed to the EFSM20 database (<a href="https://www.seismofaults.eu/efsm20">https://www.seismofaults.eu/efsm20</a>).</p>
Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
OHHR – The Oldenburg Hearing Health Record [Dataset]
<p><strong>Description of the dataset</strong></p> <p>The Oldenburg Hearing Health Record (OHHR) provides a publicly accessible dataset that can be used to advance hearing health research. It includes a constellation of data collected from 581 participants (aged 18–86 years<em>; </em>255 female; <em>Mean age = 67.31 years; SD = 11.93</em>) between 2013 and 2015 at the Hörzentrum Oldenburg in collaboration with the Cluster of Excellence "Hearing4all". The data was anonymized in accordance with the General Data Protection Regulation (GDPR; Regulation (EU) 2016/679). Each participant was assigned a unique identifier to maintain anonymity while enabling multivariate individualized analyses. </p> <p>All the different data types are listed below:<br><br><strong>Subjective Measures</strong></p> <ul> <li>Home Questionnaire</li> <li>SF-12 Health Survey</li> <li>Technology Readiness Questionnaire</li> <li>Anamnesis</li> </ul> <p><strong>Audiological Tests</strong></p> <ul> <li>Pure Tone Audiometry</li> <li>Adaptive Categorical Loudness Scaling</li> <li>Digit Triplet Test (Speech Reception Threshold in Noise: Screening)</li> <li>Göttingen Sentence Test (Speech Reception Threshold in Noise)</li> </ul> <p><strong>Cognitive Measures</strong></p> <ul> <li>DemTect</li> <li>WortSchatz</li> </ul> <p><strong>Demographic Information</strong></p> <ul> <li>Socio-economic data</li> <li>Scheuch-Winkler Index (calculated)</li> </ul> <p><strong>Supporting Documentation</strong></p> <p><strong>MethodsDescription.rtf/.pdf:</strong> Provides detailed explanations of data type and collection procedures.<br><strong>data.zip/metadata</strong><strong>: </strong>Includes schema and description files for all data tables.</p> <p>A supporting paper was published on Scientific Data:</p> <div> <div> <p>Jafri, S., Berg, D., Buhl, M. <em>et al.</em> The Oldenburg Hearing Health Record (OHHR). <em>Sci Data</em> <strong>12</strong>, 1546 (2025). https://doi.org/10.1038/s41597-025-05884-y</p> </div> </div>
Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"
<p>High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of was developed and used for estimation of of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.</p>
Indicative distribution map for Ecosystem Functional Group MT2.2 Large seabird and pinniped colonies
<p>This archive contains indicative distribution maps and profiles for <strong>MT2.2 Large seabird and pinniped colonies</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Presence observations for six tree species prioritized for forest landscape restoration in Ethiopia
<p><strong>Description:</strong></p><p>Geolocations of presence occurrences for a selection of six species (<i>Cordia africana</i>, <i>Croton macrostachyus</i>, <i>Eucalyptus globulus</i>, <i>Faidherbia albida</i>, <i>Grevillea robusta</i>, <i>Juniperus procera</i>) sourced from databases (GBIF, RAINBIO) and from the scientific literature.</p><p>Each record is associated with a DOI, link, or citation to the original source of the data. Observations were filtered using the R package <i>CoordinatesCleaner</i> (Zizka <i>et al</i>. 2019) with the <i>clean_coordinates </i>function to filter for errors that are common to biological collections.</p><p>The breakdown of the number of observations by species is: <i>Cordia africana</i> (84); <i>Croton macrostachyus</i> (129); <i>Eucalyptus globulus (</i>20); <i>Faidherbia albida </i>(31); <i>Grevillea robusta </i>(350); <i>Juniperus procera </i>(115).</p>
Data and Code Accompanying "Retrieving and Analyzing Taste Colexifications from Lexibank"
<p>Data and Code accompanying the study "Retrieving and analyzing taste colexifications from Lexibank" by Olena Shcherbakova and Johann-Mattis List (see <a href="https://calc.hypotheses.org/6398">https://calc.hypotheses.org/6398</a>).</p><p>Information on how to run the code can be found in the study itself.</p>
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
PolarFront cruise 2023-08 ship logs from Helmer Hanssen
<p><strong>PolarFront 2023-08 ship logs</strong> </p><p>Original (ISO 8859-1 encoded) text files from the ship logger on Helmer Hanssen.</p>
Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Accessible Oceans: Auditory Display. Ocean Response to Extratropical Storm Hermine
<p>The twenty-one tracks make up an auditory display of the ocean response to extratropical storm Hermine in 2016. The tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/VGWHypmrFPZ1NqzeqZuM">listen online here</a>.</p> <p>The ocean data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the <a href="https://datalab.marine.rutgers.edu/ooi-nuggets/extratropical-storm-hermine/">OOI Nugget</a> developed by Dr. Leslie Smith. Please note that the Ocean Labs data nugget does not include sea wave height as part of its graph that we sonified. Storms also impact sea wave height, and Dr. Leslie Smith acquired this data from the OOI so that we could include it in the sonification and auditory display.</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Dynamic Reconstructions of Sagittarius A* with Resolve from 2017 EHT data
<p>This repository contains the dynamic reconstructions of Sagittarius A* (SgrA*) from (EHT) data using the Resolve framework, as presented in "Resolving Horizon-Scale Dynamics of Sagittarius A*".</p>
Another-Trial-of-Depositor-0003
<p>This deposition is best described in the following terms... extending the sentence here, just so that it is obvious that one can add paragraph(s) of text.</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.