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Data for "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy"
<p>This dataset contains data used in the publication entitled "<strong>Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy</strong>". This publication concerns how β'' precipitates are sheared by dislocations during deformation. The data contained in this repository are data acquired on various transmission electron microscopes of specimens of the aluminium alloy AA6060 in peak aged condition after uniaxial compression to 5%, 10%, and 20%, in addition to the undeformed reference alloy.</p> <p>There are five main types of data:</p> <ul> <li>Transmission electron microscopy (TEM) images</li> <li>High-resolution TEM images</li> <li>High angle annular dark field (HAADF) scanning TEM (STEM) images</li> <li>Scanning precession electron diffraction (SPED) data.</li> <li>Cross-sectional data of precipitates in undeformed and 20% compressed conditions.</li> </ul> <p>Data for the TEM, HRTEM, and STEM images are kept in zipped folders due to the large number of images (several hundreds for each compression condition). Folders are named following the format of "<alloy>_<compression>_<technique>", where technique refers to TEM, HRTEM, or STEM. Images are provided in both .hdf format and .jpg format (to aid in navigating the data). Please see <a href="https://www.hdfgroup.org/">HDF Group</a> for more information regarding the HDF file format, and <a href="https://www.hdfgroup.org/downloads/hdfview/">HDF View</a> for softaware to read and show HDF data. The Python package <a href="http://hyperspy.org/">HyperSpy</a>, is also useful for loading the HDF data for inspection, analysis, and presentation.</p> <p>For some STEM images, a stack of short-exposure STEM images acquired and analysed using the <a href="http://lewysjones.com/software/smart-align/"><em>SmartAlign</em></a> plugin to <a href="http://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software"><em>Gatan Digital Micrograph</em></a> is available. SmartAlign offers the possibility of rigidly and non-rigidly aligning the STEM images in the stack in order to reduce effect of specimen drift and scan noise during acquisition. The conventional STEM images are found in the zip archive labelled "STEM". When the filenames of the STEM images include "SAstack" and/or "SAimage", a STEM SmartAlign stack or the average through a non-rigidly aligned stack is available of the same field of view. In such cases, both the SmartAlign stack and the through-stack image is provided in the metadata in the .hdf file (note that not all stacks have been aligned, and in such cases no through-stack image is available). In addition, the SmartAlign stacks themselves are available in the subfolder "STEM\SmartAlign\" within each STEM folder. The through-stack images of the smart align stacks are also provided separately in the subfolder "STEM\SmartAlign\Aligned\". For the 20% compressed case, a lowloss electron energy loss spectroscopy (EELS) spectrum and thickness maps of the imaged areas are also provided, in the subfolder "STEM\EELS\".</p> <p>The SPED data, acquired using the <em>ASTAR</em> system of <em><a href="https://www.nanomegas.com/">NanoMegas</a></em>, is provided as .hdf5 files in the root directory of the repository. They should be read using and <a href="https://github.com/pyxem/pyxem">pyXem</a>. The attached Jupyter Notebook "SPED_data_inspection.ipynb" can be used to access the SPED datasets. These datasets are 4D datasets, with two spatial and two reciprocal dimensions. They have been decomposed using the non-negative matrix factorization algorithm (NMF) used in HyperSpy. These decomposition results are included in the .hdf5 files. In addition, parameters used in the preprocessing of the datasets are attached in the metadata in these files. The metadata of these files are also provided separately as .txt files.</p> <p>Finally, measurements of the precipitate cross-sectional area and circularity is available as .csv files with the first column being the row index, the second the cross-sectional areas of precipitates measured in nanometers squared, the third column is the perimeters of the precipitates measured in nanometers, and column four is the <a href="https://imagej.nih.gov/ij/plugins/circularity.html">circularity</a> of the precipitates.</p>
Pauni (पौनि Bhandārā district) Maharashtra. Hardolal Tekri, findspot of the inscription of Bhagadatta
<p>Hardolal Tekri from east </p> <p>Location: 20°47'40.49"N 79°37'26.06"E</p> <p>The mound was excavated by S.B. Deo and J.P. Joshi in 1969-70 (Deo & Joshi 1972) where it is reported as the findspot of the inscription of Bhagadatta. Numerous brickbats, sandstone fragments and iron slags are visible on the surface.</p>
Non-refractory particulate sulfate and chloride data from a time of flight aerosol chemical speciation monitor around the Southern Ocean in the austral summer of 2016/17, during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE) campaign was conducted between 20th December 2016 and 19th March 2017. The time of flight aerosol chemical speciation monitor (ToF-ACSM, Aerodyne Research Inc.) was deployed. It is capable of providing 10-minute resolution chemical compositions of NR-PM1 (non-refractory particulate matter with aerodynamic diameter smaller than 1 µm), including sulphate, nitrate, ammonium and organics. Chloride is refractory and can only be measured qualitatively, that is relative changes in intensity are trustworthy while absolute concentrations are a clear underestimation, because most of the chloride is in refractory form as part of sea salt in the marine environment. Since this ACSM dataset was collected on the ship, the ship exhaust will occasionally interfere with the natural signal. Therefore data gaps exist. The overall concentrations of particulate organics, nitrate and ammonium remained low, mostly below detection limit, except during the polluted periods. Thus, we do not report these three components. Only sulphate can be retrieved as a quantitative variable from this dataset.</p> <p>This dataset provides limited information on the chemical composition of sub-micron non-refractory aerosol in the Southern Ocean and gives hints on potential sources. Chloride clearly reflects the contribution of sea salt to the aerosol population. This can be checked by relating the particulate chloride to wind speed (Landwehr et al., 2019; 10.5281/zenodo.3379590) and particles with large diameters (Schmale et al., 2019; 10.5281/zenodo.2636709). Particulate sulphate may originate from a variety of sources: sea salt (minor contribution), anthropogenic emissions and natural marine emissions of dimethylsulfide, which is converted to SO2 and sulphuric acid in the atmosphere and can subsequently partition into the particle phase via gas-phase or aqueous phase reactions (Schmale et al., 2019).</p> <p><strong>Dataset contents</strong></p> <ul> <li>raw_chl_SO4_mz_55_57_manual_with_flags.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>calibration_info.csv, metadata, comma-separated values</li> </ul>
Seawater dissolved chromium concentration, redox speciation, and stable isotope composition in the North Pacific Ocean
<p>Dissolved seawater chromium concentrations, redox speciation, and stable isotope composition were measured on samples collected in the North Pacific Ocean. Samples were collected over diel cycles (2 for stations 1-5, 1 for station 6) on board the RV Kilo Moana cruise KM1713 from Seward Alaska to Honolulu Hawai’i. Sampling stations spanned the subarctic North Pacific (stations 1 & 2), the dynamic subarctic-subtropical convergence zone (stations 3 and 4) and the subtropical North Pacific (stations 5 and 6). Chromium was enriched from filtered samples by Mg(OH)<sub>2</sub> co-precipitation and analyzed by MC-ICP-MS using either isotope dilution (Cr redox speciation) or double spike methodology.</p>
Dataset of "Photoelectrochemical generation of H2O2 using hematite (α-Fe2O3) and gas diffusion electrode (GDE)"
<p>In contrast to the industrial-scale production of H2O2 the electrochemical or photoelectrochemical synthesis is environmentally friendly. In the present work, <br>the photoelectrochemical generation of H2O2 was studied by combining the hematite (α-Fe2O3/FTO/glass) photoanode and gas diffusion electrode (GDE) modified by <br>incorporation of tin (II) phthalocyanine (SnPc) in its hydrophilic layer. The experiments were carried out in a photoelectrochemical cell with two compartments <br>separated by a proton exchange membrane under applied bias and AM1.5 irradiation (100 mW/cm2). The generated amount of H2O2 was determined by chemical analysis <br>(visible light spectrophotometry) of the electrolyte. As a tool to determine the efficiency of such a process, the Faradaic efficiency (FE) was calculated. The <br>best configuration used air as an inlet gas for GDE and phosphate buffer (pH 6.4) as an electrolyte in the cathodic compartment. The combination of hematite and <br>GDE (with SnPc) was the most effective in H2O2 photoelectrochemical generation. The highest value of FE was 52.4 % for GDE (O2 reduction to H2O2) and 0.4 % for <br>hematite photoanode (H2O oxidation to H2O2).</p>
Shapefile of administrative boundaries in Glasgow, UK, around 1920
<p>This dataset consists of a shapefile of administrative boundaries (municipal wards) in Glasgow around 1920, based on 'Map of the City of Glasgow shewing Parliamentary Divisions as fixed in 1918, and Municipal Wards as fixedin 1920'. The map is held in the Glasgow City Archives, reference DTC/13/98.</p> <p>Shapefile construction was undertaken as described in the related article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>The attributes which are included in the shapefile are Ward_Num (municipal ward number) and Ward_Name (municipal ward name). Full details of the wards numbers and ward names are given in the Report of the Medical Officer of Health for the City of Glasgow for 1921, which can be accessed at<em> https://wellcomecollection.org/works/jxgvafxr/items. </em></p>
Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent
<p>This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). <br><br>For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>.</p> <p>The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.<br><br>For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:</p> <p>bioclim01 = Annual Mean Temperature<br>bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))<br>bioclim03 = Isothermality (BIO2/BIO7) (×100)<br>bioclim04 = Temperature Seasonality (standard deviation ×100)<br>bioclim05 = Max Temperature of Warmest Month<br>bioclim06 = Min Temperature of Coldest Month<br>bioclim07 = Temperature Annual Range (BIO5-BIO6)<br>bioclim08 = Mean Temperature of Wettest Quarter<br>bioclim09 = Mean Temperature of Driest Quarter<br>bioclim10 = Mean Temperature of Warmest Quarter<br>bioclim11 = Mean Temperature of Coldest Quarter<br>bioclim12 = Annual Precipitation<br>bioclim13 = Precipitation of Wettest Month<br>bioclim14 = Precipitation of Driest Month<br>bioclim15 = Precipitation Seasonality (Coefficient of Variation)<br>bioclim16 = Precipitation of Wettest Quarter<br>bioclim17 = Precipitation of Driest Quarter<br>bioclim18 = Precipitation of Warmest Quarter<br>bioclim19 = Precipitation of Coldest Quarter<br>huss = Average (arithmetric mean) specific humidity<br>rsds = Average (arithmetric mean) Surface downwelling shortwave radiation<br>sfcwind = Average near-surface wind speed (arithmetric mean)<br><br>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> <p><br>All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse. </p>
Data set of the manuscript titled: Follicular Immune Landscaping Reveals a distinct profile of FOXP3hi CD4+ T cells in Treated compared to Untreated HIV
<p>Multiplex imaging data were collected using a scanning confocal system (STELARIS, Leica) and proccessed with the Imaris and Fiji imaging programs. csv files incuding the position identifiers and intensities for each fluorochrome used were generated and data were further analysed using the FlowJo10 program. Neighboring analysis was performed using the G function and mean of minimum distances of relevant cell type pairs. </p>
Four Essential Components for FAIR Data: Capability & Category-Specific Requirements
<p>Adapted from Bailo (2019) and Peng (2023), this diagram illustrates FAIR requirements specific to data, metadata, and infrastructure, aligned with the definitions of individual FAIR principles. It highlights the critical role of enterprise capabilities—including processes, systems, standards, tools, and skills—in supporting FAIR data. These four components are essential for systematically enhancing the overall FAIRness of an organization's scientific data collection</p> <p> </p>
FULFILL dataset round 1 Germany
<p>This dataset and codebook correspond to the initial round of survey data gathered in Germany in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p>
LIAS light – A Database for Rapid Identification of Lichens – Subset Switzerland p. pte.
<p>This subset of the LIAS light database focuses on lichens found in Switzerland, providing comprehensive data for ecological research and taxon identification purposes. Not all taxa and only categorical (but 2 numerical) characters (descriptors) of the recorded taxa are covered. Updates and additional data will be published subsequently.</p>
PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures
<h1>PAN-AR</h1> <p>This is <strong>PAN-AR</strong> (Panoramas, Ambient Noise & Ambisonics RIRs), a dataset described in the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Filippo Denti, Davide Fantini, Federico Avanzini and Giorgio Presti. PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures. In <em>Proceedings of the 19th International Audio Mostly Conference</em>, Milan, Italy, September 2024.</p> </blockquote> <p>The dataset includes Spatial Room Impulse Responses (SRIRs) in second-order Ambisonics format, ambient noise recordings, and spherical photos. These data have been captured in four environments with different configurations of the source and listener positions:</p> <ol> <li>Printer room</li> <li>Meeting room</li> <li>Classroom</li> <li>Underground parking area</li> </ol> <p>Panoramas and planimetries are provided in a temporary version. The final version with post-processed panoramas and complete planimetries will be available soon. An example of the final panoramas is provided for position A of the printer room, while an example of complete planimetry is provided for the printer and the meeting rooms.</p> <h2>SOFA</h2> <p>The SRIRs are also provided in SOFA format <a href="https://sofacoustics.org/data/database/pan-ar/" target="_blank" rel="noopener">here</a>.</p> <h2>How to cite</h2> <p>If you use the PAN-AR dataset, please cite the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <pre><code>@inproceedings{denti2024panar,</code><br><code> title = {{PAN-AR}: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures},</code><br><code> author = {Denti, Filippo and Fantini, Davide and Avanzini, Federico and Presti, Giorgio},</code><br><code> year = {2024},</code><br><code> month = {September},</code><br><code> booktitle = {Proceedings of the 19th International Audio Mostly Conference (AM '24)},</code><br><code> location = {Milan, Italy},</code><br><code> publisher = {ACM},</code><br><code> isbn = {979-8-4007-0968-5/24/09},</code><br><code> doi = {10.1145/3678299.3678332}</code><br><code>}</code></pre>
Dataset for "Study of Rapid Capacity Fade in Prismatic Li-ion Cells with Flexible Packaging"
<p>Prismatic lithium-ion batteries (LIBs) are considered promising electric energy sources in electromobility applications due to their cell to pack density. However, their sensitivity to external and internal influences, and reduced durability lead to inflation risk and potential explosions throughout their lifecycle. These critical processes are strongly influenced by the inner construction of the cell, especially concerning the coating and mechanical fixation. This study subjects a commercially available prismatic LIB cell to comprehensive, correlative analysis employing various imaging techniques. The inner structure of the entire cell is visualized non-destructively by X-ray computed tomography (CT), enabling the identification of critical design flaws prior to electrochemical cycling. Electrochemical cycling simulates the battery lifecycle, and the cell is subsequently disassembled in the fully charged state. The usage of the inert-gas transfer system allowed the preparation of Broad Ion Beam (BIB) electrodes cross-sections in a fully native state and for the first time to observe the tearing of graphite particles due to over-lithiation. Established region labeling system allowed to use CT and scanning electron microscopy (SEM) correlatively to identify critical regions. After 100 cycles, a 40% capacity loss was observed and event diagram describing deagradation mechanisms, related both to the cell design and to the processes occurring at high load, was created.</p>
Dataset of "Single-step Purification and Characterization of Pseudomonas aeruginosa Azurin"
<p>Azurin is a small periplasmic blue copper protein. We have created a novel approach for expressing and purifying of azurin in E. coli with high yields and optimal metalation ratio. The azurin sequence was N-terminally fused with a GST tag and protein was purified by single-step affinity chromatography on a GST-trap column. The N-terminal tag was cleaved off by HRV 3C protease and sufficient metalation was endured by incubation with copper sulphate. UV-VIS absorption, mass spectroscopy, and circular dichroism analysis all validated the effective production of azurin, appropriate protein folding and the development of an active site with an associated cofactor. MD simulations verified that incorporation of the N-terminal GPLGS segment does not affect the wild-type azurin structure.</p>
Dataset of "Sensitivity analysis in photodynamics: How the electronic structure controls cis-stilbene photodynamics?"
<p>The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, inaccuracies in nonadiabatic dynamics can lead to misinterpretations, particularly when calculated observables exhibit low sensitivity to the underlying dynamics. This issue is exemplified in the photochemistry of cis-stilbene, where similar experimental outcomes have been differently interpreted based on the electronic structures supporting nonadiabatic dynamics. This study examines the predictions of cis-stilbene photochemistry using trajectory surface hopping methods coupled with various electronic structures (OM3-MRCISD, SA2-CASSCF, XMS-SA2-CASPT2, and XMS-SA3-CASPT2) and assesses their ability to interpret experimental observations. Although the excited-state lifetimes show consistency, ranging from 360 fs to 295 fs, the reaction quantum yields vary significantly. The quantum yield for cyclization ranges from nearly zero to 35% while the photoisomerization channel can either exceed 50% or be entirely suppressed completely in the second case. Intriguingly, the calculated photoelectron signal is not strikingly different for different reaction scenarios, making the methods seemingly reliable when treated separately Furthermore, analyzing stationary points on the potential energy surface does not reliably predict simulation outcomes, nor does it aid in selecting a specific method before simulations. Therefore, we advocate for incorporating sensitivity analyses in the simulation protocol. While employing an ensemble of methods is impractical, nonadiabatic simulations with external bias present a resource-efficient approach to achieve this goal.</p>
Microscopic trip chains for Brunswick (Germany) region
<p>The data set contains microscopic trip chains for the Brunswick (Braunschweig) area in Germany on an average day. All synthetic persons within Braunschweig are shown, as well as all households outside Braunschweig where at least one synthetic person had an activity in Braunschweig.</p> <p>The generation of this data set is based on a two-stage process. The starting point is the macroscopic transport demand model DEMO (Winkler and Mocanu, 2020: https://doi.org/10.1016/j.trd.2020.102476) and a population upscaled from the MiD 2017 ("Mobilität in Deutschland") for Germany, which was spatially distributed according to the BKG household dataset (households, inhabitants, federal government). In the first step of the process, the trip chains between the DEMO traffic cells were generated based on the daily schedules of the MiD population (Mocanu and Joshi, 2022: https://elib.dlr.de/188443/). In the second step of the process, corresponding locations were assigned within the target traffic cells. The locations were previously extracted from OpenStreeMap and attributed with activities according to their attributes/metadata (key/value pairs) (Malkus et al., 2024: https://doi.org/10.1016/j.procs.2024.06.043).</p>
Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
Dataset of "Smart Grids Transmission Network Testbed: Design, Deployment, and Beyond"
<p>Our test environment incorporates a unique blend of physical, emulated, and virtualized<br>components, spanning from electrical substations to SCADA systems,<br>thereby offering a versatile platform for testing against cyber threats, facilitating<br>educational programs, and supporting advanced traffic simulation. Key findings<br>from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities,<br>enhancing cybersecurity measures, and providing valuable hands-on<br>learning experiences. The integration of such diverse components not only exemplifies<br>a significant step forward in testbed design but also showcases its potential<br>in fostering innovation and security in the power sector. Through detailed comparisons<br>with existing testbeds, we underscore our testbed’s distinct features<br>and its contribution to bridging the gap in current methodologies, setting a new<br>benchmark for future developments in smart grid testing and education.</p>
Dataset of "Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges"
<p>Industrial networks are adapted to their specific requirements, especially in terms of industrial processes. To ensure sufficient security in these networks, it is necessary to set and use security policies that complement government regulations, recommendations, and relevant security standards. This paper aims to provide an in-depth analysis of the anomalies occurring within the networks and propose a structure for collecting valuable data from the experimental site based on dividing anomalies into three main categories:<br>security, operational, and service anomalies (and regular traffic recognition). We present a proof-of-concept solution/design aggregating data in industrial networks for advanced anomaly classification. Multiple data sources such as industrial communication, sensor data (additional sensors controlling device behavior), and HW status data are used as data sources. A total of three scenarios (using a physical testbed) were implemented, where we achieved an accuracy of 0.8540/0.9972 in advanced anomaly classification.</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.