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4,769 results for “protection”
Phenological metrics for Protected Area "NorthernLimestone", MODIS terra tile h18v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "DanubeDelta", MODIS terra tile h20v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "Hardangervidda-h18v03", MODIS aqua tile h18v03
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "WaddenSea", MODIS aqua tile h18v03
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "PenedaGeres", MODIS terra tile h17v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "MurgiaAltaPark", MODIS aqua tile h19v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
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>
Low-Voltage Icing Protection Film for Automotive and Aeronautical Industries
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</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>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
volatile organic compounds that were measred in various sites in Israel, available by the Israel Ministry of Environmental Protection
<p>The dataset comprises measurements of volatile organic compounds sampled at multiple sites across Israel from 2010 to 2024, encompassing urban, rural, and suburban locations.</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>
Identifying South African Marine Protected Areas at risk from marine heatwaves and cold spells
<p>This data reflects information on marine heatwaves (MHWs) and marine cold spells (MCSs) that occurred along the South African coast from January 1982 to April 2022, with special focus on Marine Protected Areas. Thermal metrics for MHW and MCS events were obtained using the HeatwaveR package (Schlegel and Smit, 2018) and the associated Marine Heatwave Tracker (Schlegel, 2020). </p> <p> </p> <p>THis data stems from Courtailac et al (in review) Indentifying South AFrican Marine Protected Areas at risk of marine heatwaves and cold-spells </p>
Database for Perceived Inclusivity and Trust in Protected Area Management Decisions among Stakeholders in Alaska
<p>This database is part of a state-wide survey in Alaska, USA. An online Qualtrics interface was used to administer the survey to a panel of Alaskan residents from June to August 2020.</p>
Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats - Datasets and supporting files
<p>The supporting datasets, scripts, and supplementary information for the manuscript, "Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats," are available within this repository.</p> <p>We conduct an analysis on the coverage of protected areas that cover six threatened marine and coastal and developed two indexes, the Local Proportion of Habitat Protected Index and the Global Proportion of Habitat Protected Index, describing the protection of these habitats locally and globally. The habitats considered are the following: cold corals, warm water corals, knolls and seamounts, mangroves, saltmarshes, and seagrasses.</p> <p>The index scores of each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes_average.csv</em></p> <p>The habitat specific index scores for each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes.csv. </em></p> <p>Column name descriptions are available in the text file: <em>Column_name_descriptions_20220301</em></p> <p>The scripts used to run the workflow to calculate the indexes, create figures, and calculate statistics for the manuscript are also included. The script <em>01_Workflow sources</em> the first 9 scripts in the <em>scripts</em> folder to calculate the indexes which relies on the functions script within the functions folder. The rest of the scripts in the folder create the figures and calculate the statistics for the manuscript.</p> <p>A readme pdf file is included here to ease with reproducing the workflow, but we strongly suggest to please visit our github (<a href="https://github.com/jkumagai96/Marine_Habitat_protection">https://github.com/jkumagai96/Marine_Habitat_protection</a>) to reproduce the entire calculation where we provide detailed information on how to run the workflow and package management.</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.