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458 results for “Data Protection”
Academics for Land Protection in New England (ALPINE) GIS Data 2015-2018
Academics for Land Protection in New England (ALPINE) is a network of academic institutions committed to increasing the pace of land protection in New England to address the region’s environmental challenges and to support nature and people. ALPINE seeks to expand the role that academic institutions play in conserving the New England landscape by sharing experiences and resources among faculty and staff, students, administrations, and alumni. This dataset contains point locations of colleges that are participants in ALPINE and parcels of natural land owned by participating schools who submitted data to the ALPINE coordinator.
Water, Soil, Floc, Plant Total Phosphorus, Total Carbon, and Bulk Density data (FCE) from Everglades Protection Area (EPA) from 2004 to 2016
These data are a compillation of data from multiple sources including South Florida Water Management District (SFWMD) DBhydro web database, United States Environment Protection Agency Regional, Environmental Monitoring and Assessment (REMAP), Everglades Soil Mapping (ESM), and Florida Coastal Everglades Long Term Ecological Research (FCE-LTER). The matrix of these data were compiled for soil, surface water, floc, and plants where the nutrients are counted for total phosphorus, total carbon, and bulk density. When downloading the data from DBhydro, only regularly collected samples (SAMP) were included these data. As per DBhydro metadata, the regular samples were collected monthly by grab method throughout the year from 2004 to 2016 for SFWMD monitoring stations across the EPA. All flagged and field quality controlled values were excluded to avoid the duplication of data. In order to maintain the quality assurance/ quality control (QA/QC) the method detection limit for water TP was fixed at 2 µg/L by the SFWMD. This data set were used to assess the decadal trend of TP concentration in surface water and soil in EPA. Available data from 2004 to 2014 was collected for soils and from 2004 to 2016 for water to understand a decade of trends. Both Geographic Information System (GIS) and statistical data analysis were applied to determine changes in water quality and soil chemistry. These data are the basis for Shishir Sarker's Master's thesis.
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
Data from Bieri Thesis: Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs - 2022
This dataset consists of spreadsheets used in the creation of: "Elizabeth Bieri, Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs. MS Thesis, University of Virginia, Charlottesville, VA. Advisors: Matthew Reidenbach & Patricia Wiberg, 2022" (https://doi.org/10.18130/2b04-wz72). Documentation on methods and types of data are given in the thesis and are not repeated here. There are two .zip files. One contains the original Excel workbooks. The other contains the data from individual sheets within the workbooks as comma-separated-value (.csv) text files. The file listing for the CSV files are: Archive: Bieri_Comma_separated_Value_Files.zip Length Date Time Name --------- ---------- ----- ---- 0 2025-12-15 12:47 Bieri_Comma_separated_Value_Files/ 1787 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_crests.csv 239 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S4.csv 273 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S7.csv 652 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed.csv 476 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed_no_reef.csv 102 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S4.csv 97 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S7.csv 297 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_july.csv 290 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_oct.csv 399 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_All.csv 509 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_July2021.csv 507 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Oct2021.csv 1242 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sed_OM.csv 516 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sept2020.csv 913981 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS4.csv 715881 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS7.csv 5251
Replication Materials for Disclosure Limitation and Confidentality Protection in Linked Data
<p>These are the data and derived figures as used in the chapter by Abowd, Schmutte, and Vilhuber, "Disclosure Limitation and Confidentiality Protection in Linked Data"</p>
Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal. <em>Journal of Affective Disorders</em>. </strong></p> <p><em>Background:</em> To investigate the powerful placebo effects in antidepressant drug trials and their mechanisms, recent pioneering experimental studies showed that expectation manipulation combined with an active placebo attenuated induced sadness. In the present study, we aimed at extending these findings by assessing the psychophysiological response in addition to mere self-report.</p> <p><em>Methods:</em> One hundred thirteen healthy female students were randomly assigned to a drug expectation group (active placebo, positive treatment expectation), placebo expectation group (active placebo, no treatment expectation), or a no-treatment group (no placebo, no treatment expectation). After placebo intake, sadness was induced by self-deprecating statements using the Velten method combined with sad music, including a rumination phase. Sadness was measured using the Positive and Negative Affect Schedule Expanded Form (PANAS-X). Heart rate and skin conductance were assessed continuously.</p> <p><em>Results:</em> After mood induction and after rumination, self-reported sadness was significantly lower, and skin conductance level was significantly higher, in the drug expectation group than in the no-treatment group. The mood induction was further accompanied by a heart rate deceleration within all groups.</p> <p><em>Limitations: </em>Generalizability is limited by sample selectivity and focusing on sadness as a symptom of depression, exclusively.</p> <p><em>Conclusion:</em> Expectation-induced placebo effects significantly influenced sadness-correlated changes in autonomic arousal, and not only subjectively reported sadness, indicating that placebo effects in the context of affect are not merely due to subjective response bias. The systematic modification of treatment expectation could be utilized in clinical practice to optimize current therapeutic approaches to improve mood regulation.</p>
Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment
<p>We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely, EC-Earth3-Veg, IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia.</p> <p>All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models.</p> <p>Further details and all code associated with these datasets are found <a href="https://github.com/PlekhanovaElena/Arctic_vegetation_prediction">here</a>.</p>
Documentary sources of case studies on the issues a data protection officer faces on a daily basis
<p>The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].</p> <p>The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.</p> <p>The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.</p> <p>The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).</p> <p>The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified.<br> The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.</p> <p>The available files are as follows:</p> <ul> <li><strong>documents-texts.zip</strong> --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.</li> <li><strong>documents-metadata.csv</strong> --> Contains a CSV file with the metadata for each document used as a source for the case studies.</li> </ul> <p>This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].</p> <p>[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.</p> <p>[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.</p> <p>[3] R. K. Yin, Case study research and applications. Sage, 2018.</p>
figure data for "Subsurface radiation environment of Mars and its implication for shielding protection of future habitats" by L.Röstel, J.Guo et al. 2020
<pre>This data of dose rates at different elevations above and below the Martian surface was modeled using the GEANT4-based AtRIS toolkit. Please refer to the following paper for reference and a detailed description of the model and scaling: „Subsurface radiation environment of Mars and its implication for shielding protection of future habitats“, L.Röstel, J.Guo et al. 2020 JGR: planets. List of files: AbsorbedDosePrimariesAR.txt - figures 2 in the paper EquivalentDosePrimariesAR.txt - figure 3 AbsorbedDoseSiliconSlabScenarios.txt - figure 4 AbsorbedDoseWaterSphereScenarios.txt - figure 5 EquivalentDoseWaterSphereScenarios.txt - figure 6 NeutronFlux.txt - figure 7 RequiredShieldingDepth.txt - figure 8</pre>
TERMINUS WP4: Enzyme immobilization, protection, and triggering. TASK 4.2: Experimental data
<p>A. E. Delorme, J.-M. Andanson and V. Verney: Improving Laccase Thermostability with aqueous Natural Deep Eutectic Solvents, <em>Int. J. Biol. Macromol.</em>, (2020), <a href="https://doi.org/10.1016/j.ijbiomac.2020.07.022">doi.org/10.1016/j.ijbiomac.2020.07.022</a></p> <p><strong>Abstract</strong></p> <p>The wide-spread use of laccases in industry is often limited due to the enzyme inactivation over time at conditions which exceeds the operating conditions of the enzymes, which are neutral pH and ambient temperatures (30-40 °C). Today, the most common strategies used to improve enzyme stability are chemical modifications and immobilization of enzymes on solid supports. Although, these techniques have shown promise in improving enzyme stability, they are often synthetically demanding and unsustainable in terms of costs and synthesis route. Deep Eutectic Solvents (DESs) have attracted considerable attention as reaction media in biocatalysis due to their promising compatibility with enzymes and sustainable derivation. In this contribution we demonstrate the possibility of applying DESs as incubation media to inhibit thermal inactivation of laccase T. Versicolor. For example we show that by incubating laccase in 25 wt% of a betaine-xylitol based DES at 70 °C for 15 minutes, the measured residual activity of laccase is a near 10 fold greater than the measured residual activity of laccase when incubated without the DES. The drastic enhancement of the enzyme thermostability by pre-incubation of laccase in DES media showcases a facile, cheap and green method of boosting the stability laccase.</p> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset contains all the UV-kinetic raw data used to calculate the laccase activity in the article “Improving Laccase Thermostability with aqueous Natural Deep Eutectic Solvents”. Data are available in a compressed .zip file with 1 folder (Laccase-thermostability-DES_v1.0_TER_WP4_D4-2) containing 3 files:</p> <p> </p> <ul> <li>one tabular file saved in .xlsx format containing all UV-kinetic raw data used to calculate the relative and residual laccase activities for figure 1-6 in the article (<a href="https://doi.org/10.1016/j.ijbiomac.2020.07.022">doi.org/10.1016/j.ijbiomac.2020.07.022</a>). The Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx file contains the UV kinetic absorption spectra (at wavelength 417 nm) and each row in represent one spectrum. The spectra are grouped under laccase incubation temperature and length of time of incubation. For each incubation time, three solutions were prepared which signifies the three trials under each incubation times. Each sheet in the .xlsx file represent the data set collected for each laccase incubation medium</li> <li>The Materials_and_experimental_method-D4-2-Laccase-Thermostability-DES.pdf file details the experimental method and conditions for the data acquisition presented in the Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx. Guidance is also provided on how to use the data to calculate the laccase activity and thermostability.</li> <li>The_metadata_information-D4-2-Laccase-Thermostability-DES.pdf includes more detailed metadata information for the dataset represented in the excel file Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx.</li> </ul>
PROTECT project second RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Contact person(s)</strong>: Enrico de Marinis</p> <p><strong>Data collector(s)</strong>: Enrico de Marinis. Fabrizio Pucci, Michele Uliana</p> <p><strong>Data curator(s)</strong>: Guido Rosi</p> <p><strong>Work package leader(s)</strong>: Fabrizio Pucci; Fabio Andreucci</p> <p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_143538_000002_000003_008.decod.grz; collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_150213_000024_000024_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_153840_000007_000024_005.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_155152_000024_000003_010.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_113457_000024_000004_006.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_141622_000007_000007_003.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_153554_000007_000007_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> </ul> <p><strong>Images of the experimental data</strong></p> <p>For each of the above data files, the image of the corresponding PDR (Pedestrian Dead Reckoning) processed track has been added as a geo-referenced JPG capture overlaid on the location satellite image. The image file name is the same as the corresponding data file.</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.jpg</li> <li>RawData_20200729_143538_000002_000003_008.decod.jpg</li> <li>RawData_20200729_150213_000024_000024_004.decod.jpg</li> <li>RawData_20200729_153840_000007_000024_005.decod.jpg:</li> <li>RawData_20200729_155152_000024_000003_010.decod.jpg</li> <li>RawData_20200730_113457_000024_000004_006.decod.jpg</li> <li>RawData_20200730_141622_000007_000007_003.decod.jpg</li> <li>RawData_20200730_153554_000007_000007_004.decod.jpg</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.jpg</li> </ul> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all datasets have been recorded with a 200 Hz sampling frequency.</p>
TERMINUS WP4 Enzyme immobilization, protection, and triggering. TASK 4.1 TASK 4.2 Experimental data – Hydrolytic enzyme immobilization and triggering
<p>The use of polymer-degrading enzymes is an attractive and effective method for the management of plastic waste. Synthetic polyesters such as poly(ethylene terephthalate) (PET) or polyurethane (PUR) have been shown to be susceptible to enzymatic degradation by microbial polyester hydrolases, as well as biopolyesters such as poly(lactic acid) (PLA), poly(butylene succinate) (PBS), and polycaprolactone (PCL). However, raw enzymes are not used in polymer formulations because the high processing temperatures would deteriorate the proteins (whose enzymes are made of), by destroying their macromolecular structure and catalytic center. The possibility of a direct use of enzyme in a polymer formulation thorough an opportune protective system, able to preserve the activity of the enzyme and increase its thermal stability, could open to new materials degradable “on-demand” at the end-of life. Therefore, significant progresses could be possible for example in the field of plastic packaging, which currently represents 40% of the total production of plastic in EU and requires the consumption of more than 19 million tons of oil and gas.</p> <p>This dataset includes some of the experimental raw data presented by UNIBO in deliverable D4.1 and D4.3, namely FT-IR analysis, X-ray diffraction analysis, TGA analysis, UV-Vis spectrophotometer. Data are available in a compressed .zip file with 1 folder (Hydrolytic enzyme immobilization and triggering_v1.0_TER_WP4_D4.1_D4.3) containing 6 files, 4 .xlsx files containing the FT-IR, XRD, TGA, Enzyme release kinetics and Thermostability raw data, 1 .pdf file describing the experimental methods and materials and a second .pdf file outlining the metadata and information.</p> <p>1_Hydrolytic enzyme immobilization and triggering _FTIR.xlsx contains all the FTIR raw data and curves of the Immobilized enzyme systems prepared.</p> <p>2_Hydrolytic enzyme immobilization and triggering _XRD.xlsx contains all the XRD raw data and profiles of the Immobilized enzyme systems prepared.</p> <p>3_Hydrolytic enzyme immobilization and triggering _TGA.xlsx contains all the TGA raw data and curves of the Immobilized enzyme systems prepared.</p> <p>4_Hydrolytic enzyme immobilization and triggering release activity and thermal resistance.xlsx contains all the raw data and graphs related to the protein content, activity of the Immobilized enzyme systems prepared after release and the thermal stress experiment data.</p> <p>Materials and experimental method_D4.1_D4.3_Hydrolytic enzyme immobilization and triggering.pdf contains the details of the experimental method and conditions for the data acquisition presented in the data set.</p> <p>Metadata information for WP4 dataset_D4.1_D4.3_Hydrolytic enzyme immobilization and triggering.pdf includes more detailed metadata information for the dataset presented.</p>
FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas
<p>This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.</p><p>The data were created using python scripts provided at <a href="https://doi.org/10.5281/zenodo.10295920">https://doi.org/10.5281/zenodo.10295920</a>. </p><p>Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:</p><ul><li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li><li>simA, ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a></li><li>simA, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a></li><li>simA, ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a></li><li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li><li>simB: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC</a></li><li>simC, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC</a></li></ul><p> </p>
Data: Cutting the costs of coastal protection by integrating vegetation in flood defences.
<p>File: levee_crest_height_reduction_per_country_version_July2021.nc<br>Fields: (1) Crest height reduction m per km along the populated coastline susceptible to flooding (return period = 100 years)<br> (2) Crest height reduction cost saving per country in million USD<sub>2005</sub> PPP along the populated coastline susceptible to flooding (return period = 100 years)<br> (3) Cost savings as percentage of GDP<sub>2005</sub> along the urban populated coastline susceptible to flooding (return period = 100 years)</p> <p>File: transectdata_version_July2021.nc<br> Transectdata of vegetated transects within the study area.<br>Fields: <br>(1) rps = return period <br>(2) fid = id of the transects<br>(3) centroids = coordinates of the transects<br>(4) inun = (1) in area susceptible to flooding<br>(5) urban = (1) in urban area, (0) not in urban area<br>(6) veg_width = derived coastal vegetation belt width along the foreshore<br>(7) veg_type = derived coastal vegetation type along the foreshore (1: salt marshes, 2: mangroves)<br>(8) hsig = Offshore significant wave heights (multiple return periods) corresponding to the transects<br>(9) wave period = Offshore peak wave period (multiple return periods) corresponding to the transects<br>(10) surge = Extreme water level combination of surge and tide (m +MSL) (multiple return periods)<br>(11) veg_z0 = elevation at the start of the vegetated zone (m +MSL)<br>(12) hrms_end_noveg = root mean square wave height at the end of the foreshore (without vegetation) (multiple return periods)<br>(13) hrms_endveg = root mean square wave height at the end of the foreshore (with vegetation) (multiple return periods) <br>(14) pdens_15km = population density derived using buffer of 15 kilometre radius</p>
Replication data for "The uncertain future of protected lands and waters" - protected area base layer for Amazonia
<p>We created a database of terrestrial and coastal protected areas (PAs) for all nine Amazonian countries following the IUCN definition for PAs and including only state-designated and state-managed PAs. We used the best available sources of archival data, including original legal documents, to confirm information about PAs. We included PAs that currently exist, as well as those that existed previously but have been degazetted. We note that this database differs from the World Database of Protected Areas (WDPA) for several reasons:</p> <p>• we focus on nationally-designated PAs and omit international or local designations</p> <p>• we include previously protected areas</p> <p>• we exclude other area-based conservation interventions other than state-designated and state-managed PAs (such as indigenous lands, privately protected areas, recreational sites, and community based natural resource management areas) which are included in the WDPA in certain countries</p> <p>• We use the establishment date as provided in each PA’s gazettement legal document, rather than the Status Year field in the WDPA, which lists the year that the PA’s current designation was established (46)</p> <p>• We use the spatial extent as provided in each PA’s gazettement legal document, rather than the spatial extent provided in the WDPA. The spatial extent in the WDPA (Rep_Area) is reported by nations and may represent the area as measured in GIS or paper maps, rather than the legally gazetted area.</p> <p>See Table S16 for detailed information by country describing the sources of PA data used for the nine Amazonian countries. </p> <p>Citation of original paper: Golden Kroner, R. E., Qin, S., Cook, C. N., Krithivasan, R., Pack, S. M., Bonilla, O. D., Cort-Kansinally, K. A., Coutinho, B., Feng, M., Martínez Garcia, M. I., He, Y., Kennedy, C. J., Lebreton, C., Ledezma, J. C., Lovejoy, T. E., Luther, D. A., Parmanand, Y., Ruíz-Agudelo, C. A., Yerena, E., … Mascia, M. B. (2019). The uncertain future of protected lands and waters. <em>Science</em>, <em>364</em>(6443), 881–886. <a href="https://doi.org/10.1126/science.aau5525">https://doi.org/10.1126/science.aau5525</a></p>
Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas
<p><strong>Related article:</strong> Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>In this dataset:</strong></p> <p>This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning.</p> <p><strong>Please cite this dataset as:</strong></p> <p>Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>Column names</strong></p> <p>NAME: Name of protected area</p> <p>Fires_sum: Number of observed fires (VIIRS)</p> <p>Month: Month</p> <p>Year: Year</p> <p>Precipitation: Precipitation (mm)</p> <p>Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago</p> <p>YEAR_CREAT: Year of establishment of protected area</p> <p>Biome: Biome</p> <p>REP_AREA: Area of protected area (km<sup>2</sup>)</p> <p>Fires_per_km2: Fires per km<sup>2</sup></p> <p>Prec_acc_12m: Accumulated precipitation during the last 12 months</p> <p>fBiome: Biome as factor</p> <p>fNAME: Name as factor</p> <p>sPrecipitation: Precipitation (scaled; see Methods section of article)</p> <p>sPlag_1: Precipitation in previous month (scaled; see Methods section of article)</p> <p>sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article)</p> <p>Pred_Zinb_1a: Predicted fires (see Methods section of article)</p> <p>Diff_Zinb_1a: Difference: Observed fires - predicted fires</p> <p>Year_pred: Year for prediction</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Survey with game development companies on personal data protection
<p><strong>Dataset linked to the article: </strong>Investigating the Implementation of Data Protection Laws in Brazilian Game Companies: An Initial Study</p>
Derived Data from "Expanding European protected areas through rewilding"
<p>We present the major derived data obtained through the study "Expanding European protected areas through rewilding" published in Current Biology.</p> <p>Data refer to three shapefiles and it is structured as: </p> <p>1) "Rewilding Patches" folder - presenting European rewilding patches (human footprint <=5), classified by area</p> <p>2) "Marxan Solutions" folder - presenting optimized solutions to expand current European protected areas through rewilding such to achieve ,in each country, 30% area with protected areas ("PA_all" sub-folder) and 10% area with strict protected areas ("PA_strict" sub-folder)</p> <p>For detail on data, users are adviced to read the "Readme" files in each folder.</p>
Data from A functional transcriptomics analysis in the relict marsupial Dromiciops gliroides reveals adaptive regulation of protective functions during hibernation
<p>This dataset contains files with the differentially expressed genes, raw counts, DESeq2 analyses and assembled transcriptome of D. gliroides. This information is linked to the manuscript published in Molecular Ecology.</p>
ScienceDex guides
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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.