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A Daily High-Resolution (1 km) Human Thermal Index Collection over the North China Plain from 2003 to 2020
<p>The daily <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection over the <strong>North China Plain </strong>(<strong>HiTIC-NCP</strong>) includes <em><strong>near-surface air temperature</strong></em><strong> </strong>(SAT) and 11 commonly used<strong> </strong><em><strong>human-perceived temperature</strong></em><strong> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), Wet-Bulb Temperature (WBT), simplified Wet-Bulb Globe Temperature (sWBGT), and Wind Chill Temperature (WCT). This daily dataset has a high spatial resolution of 1 km × 1 km and covers the North China Plain<strong> </strong>from January 2003 to December 2020. It has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. The dataset is stacked by year and each stack consists of 365 daily images in NetCDF format by day of the year. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) to save storage space, and thus need to be divided by 100 to get the values in degree Celcius when in use. The geographic coordinate system of the dataset is World Geodetic System (WGS) 1984 Coordinate System. Naming rules and other details can be found in "README.pdf".</p> <p>If you have any questions when using the HiTIC-NCP dataset, please feel free to contact Mr. Xiang Li via <a href="mailto:lixiang97@mail2.sysu.edu.cn">lixiang97@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:zhaoyq66@mail.sysu.edu.cn">yqzhao@link.cuhk.edu.hk</a>. More details on the procedure of producing the HiTIC-NCP dataset and its accuracy assessment can be found in:</p> <p>Li, X., Luo, M*., Zhao, Y*., Zhang, H., Ge, E., Huang, Z., Wu, S., Wang, P., Wang X., Tang Y. (2023). A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020. <em>Scientific Data</em>, 10, 634. https://doi.org/10.1038/s41597-023-02535-y</p>
Triple-resolution of spectral phases via semi-relativistic ab-initio RABBITT simulations DATA & WORKFLOW
<p>This dataset contains the necessary atomic structure and input files to use the <a href="https://gitlab.com/Uk-amor/RMT/rmt">R-Matrix with Time-dependence code suite</a> (open source and freely available) to replicate the results presented in "Triple-resolution of spectral phases via semi-relativistic ab-initio RABBITT simulations".</p> <p>Additionally, the output photoelectron momentum spectra data output from the RMT simulations are provided, to allow replication of the post-processing and spectral phase extraction processes in the absence of access to a large HPC cluster.</p> <p>Finally, a link is provided to a <a href="https://gitlab.com/lukeroantree/argon_rabbitt_scripts">git repository</a> hosted on gitlab.com where post-processing, spectral phase extraction, and visualisation tools are available to operate on these momentum spectra, and an interactive example is provided via a webhosted (via mybinder) Python Jupyter notebook.</p>
Comparative performance of coarse-grained IDP models at different resolutions
<p>Files for simulations. Molsim start scripts and final outputs, MARTINI Stark start scripts (including force field files, and some intermediate data). Not all intermediate data available, but all data necessary to reproduce simulations should be included.</p> <p>MARTINI simulations follow the procedure "Dry energy minimization" -> "Solvation" -> "Salting" -> "Energy minimization" -> "NVT equilibration" -> "NPT equilibration" -> "Production Run", where the output from the previous step is the input into the next step. For some folders, the output from previous step has been copied, but not always. In case of another simulation, but using a bigger box, some files from the simulation with smaller box were re-used. Thus, if a file seems to be missing, check the folder with corresponding simulation using a smaller box.</p> <p>Some "analysis files" are included, e.g. the radius of gyration for each time step, in order to allow for the reproduction of graphs in upcoming publication, and further exploration.</p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the results of photogrammetric processing (Digital Elevation Models, Orthomosaics and subset data used for volumetric calculation and visualization) named: “DEM_1967.7z”: inside the zipped folder “1967_DEM.tif” (digital elevation model produced for the year 1967), “DEM_1984.7z”: inside the zipped folder “1984_DEM.tif” exist (digital elevation model produced for the year 1984). In addition, under “1967_Orthomosaic.7z" and "1984_Orthomosaic.7z” zipped folders, there are orthomosaic files produced namely, “1967_orthomosaic.tif” and "1984_orthomosaic.tif” for the year 1967 and 1984, respectively. The DEMs and Orthomosaics subset from the results for sites (data example 1 and data example 2) reside under "Data_Examples.zip". Accuracy of the resulted data were assessed and the extracted elevation values are under "Accuracy_assessment.zip". All DEMs and Orthomosaics are in GeoTIFF format in the Adindan UTM Zone 37 N (EPSG: 20137) projected coordinate system.</p> <p> Potential application of the presented dataset include:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p>The inputs for the above resulted DEMs and Orthomosaics are found under Zenodo repository "10.5281/zenodo.7271617". </p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the inputs used for Structure from Motion Multiview Stereo photogrammetry processing for the year 1967 and 1984 i.e Unprocessed scanned historical aerial Photographs, camera position coordinates, flight index and Ground Control Points. All the scanned historical aerial photographs data are in TIFF format except four photographs in JPEG format under a zipped folder ("1967_Scanned_HAPs_Part1.7z and 1967_Scanned_HAPs_Part2.7z" for the 1967 Historical Aerial Photographs and "1984_Scanned_HAPs_Part1.7z and 1984_Scanned_HAPs_Part2.7z" for the 1984 Historical Aerial Photographs). The "Flight_Index.Zip" contains shapefiles of the camera position and polygon of consecutive aerial photograph index; "GCP.Zip" contains text file of the GCPs used for the 1967 and 1984; and "Camera_Position.Zip" contains the file of the camera position (Label, Easting, Northing and Altitude) of each historical aerial photographs. </p> <p>The results of the above dataset could be accessible on Zenodo repository "10.5281/zenodo.7269999".</p> <p>Anyone can reuse the presented dataset to produce DEMs and Orthomosaics; and use for the following application areas:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p> </p>
High Resolution Greenspace Land Cover in Philadelphia, Pennsylvania
<p>This dataset provides a high resolution (1-m) land cover map for Philadelphia, Pennsylvania in the United States of America during the summer of 2017. This dataset was created to differentiate two types of green space in Philadelphia: tree and grass cover. The dataset includes four numerically coded land cover classes.</p> <p><strong>Input data:</strong></p> <p>This classification is derived from National Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in the State of Pennsylvania during June of 2017. To improve classification accuracy, NAIP data was stacked with Sentinel-2 level 1C 10-m and 20-m data using the .addBands() function in Google Earth Engine. For the Sentinel-2 data, a median composite was calculated from cloud-masked images collected between April and October of 2017. Sentinel-2 input bands included blue, green, red, red edge 1, red edge 2, red edge 3, near infrared, and shortwave infrared 1. An additional normalized difference vegetation index (NDVI) was calculated from the NAIP and Sentinel-2 bands using the formula:</p> <p>NDVI = (Near infrared - Red) / (Near infrared + Red)</p> <p><strong>Classification methods:</strong></p> <p>We classified the input data using a Random Forest classifier with 200 trees. Data was classified into four coded land cover classes:</p> <p>1 - Tree</p> <p>2 - Grass</p> <p>3 - Human-built structures</p> <p>4 - Open water</p> <p>8,961 land cover reference points were collected with 70% used to train and 30% to test the classifier. Results were smoothed using a 3x3 square kernel based on the mode of a pixel’s neighbors.</p> <p><strong>Accuracy:</strong></p> <p>Measures of accuracy including overall accuracy and per class user’s (UA) and producer’s accuracy (PA) of the random forest classifier were calculated.</p> <p>Overall accuracy: 93%</p> <p>Tree: UA = 89.73% PA = 93.90%</p> <p>Grass: UA = 93.41% PA = 88.21%</p> <p>Human-built structures: UA = 98.28% PA = 97.47%</p> <p>Open water: UA = 93.56% PA = 98.95%</p> <p><strong>Code link:</strong></p> <p>The Google Earth Engine code used in this analysis is publicly available.</p> <p><a href="https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f">https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f</a></p> <p><strong>Data for download:</strong></p> <p>Two files are available for download.</p> <ol> <li>Philadelphia_classification_points.zip</li> </ol> <p>Contains a shapefile of the 8,961 reference points used to train and test the classifier.</p> <p> 2. Philadelphia_Landcover_2017.zip</p> <p>Contains a GEOTIFF of the classified image over Philadelphia, Pennsylvania for the summer of 2017.</p> <p> </p>
Data from: An expanded Smithian-Spathian (Early Triassic) boundary from a reefal build-up record in Oman: Implications for conodont taxonomy, high-resolution biochronology and the carbon isotope record
<p><span>Some 2.7 Ma after the Permian-Triassic boundary mass extinction (PTME), a stepwise extinction of the nekton (ammonoids and conodonts) ended at the Smithian-Spathian boundary (SSB) during an episode of climate cooling. SSB records from continental shelves are usually affected by an unconformity, suggesting a forced regression of glacio-eustatic origin. Here, we document a new 30 m-thick SSB section from Jebel Aweri (Batain Plain, Oman) that provides an exceptionally complete and expanded record preserved in an exotic block. Most of this SSB section consists of metazoan reefal build-ups that formed in shallow water on an offshore sea mount. In Wadi Musjah (Hawasina nappes, Oman), another exotic block records the SSB in a deeper water setting represented by Hallstatt-type facies. These two sections provide a unique perspective on the early Spathian rapid re-diversification of conodonts. They led to a thorough revision of conodont taxonomy around the SSB and to the construction of the highest resolution biochronological scheme for this time interval in the Tethys. A total of five SSB sections from Oman representing both offshore sea mounts and lower slope deposits were included in a high-resolution, quantitative Unitary Associations analysis. The resulting 8 conodont biozones are intercalibrated with ammonoid zones and with the carbonate carbon isotope record ultimately placing the SSB in the interval of separation between UAZ<sub>3</sub> and UAZ<sub>4</sub>. Only the association of <em>Nv. pingdingshanensis</em> with <em>Ic. crassatus</em> can be used to unambiguously characterize the base of the Spathian.</span></p>
High‐resolution genomic analysis of four local Vietnamese chicken breeds
<p>A total of 96 individuals were sampled from four populations of Vietnamese local chickens (28 Ho, 32 Dong Tao, 18 Mong, and 18 Mia) distributed in four provinces (Bac Ninh, Hung Yen, Ha Nam, and Hanoi, respectively) in the North of Vietnam. All individuals were from the same conservation program in Vietnam, originating from either private farms or public institutions involved in this program. Neither pedigree nor phenotypic information was available. We used stratified sampling for flocks, ensuring a distance of at least 500 m between the selected flocks and random sampling of one or two individuals in each selected flock. Genomic DNA was extracted from the blood samples of all chickens. The HD array for chickens allows for the simultaneous genotyping of approximately 580,000 SNPs. This array comprises SNPs on 28 autosomal chromosomes, two sex chromosomes (Z and W for poultry), and two linkage groups (LGE64 and LGE 22C19W28). Only SNPs on the 28 autosomes were considered in the present study. The SNPs with more than 10% missing genotypes or minor allele frequencies lower than 1% were removed, yielding a consensus panel of 454,297 autosomal SNPs for the analysis of population genetic structure.</p> <p>We applied a second quality control within each breed. Specifically, SNPs with within-breed minor allelic frequencies lower than 0.05 or SNPs with significant (P < 0.0001) deviation from the Hardy-Weinberg equilibrium were filtered out. This second filter led to 368,652, 383,792, 405,535, and 432,631 SNPs for Ho, Dong Tao, Mong, and Mia breeds, respectively. Of note, removing monomorphic SNPs within breeds may lead to the elimination of SNPs that are polymorphic across breeds. Due to the large number of available SNPs, we did not consider the potential effect of these filtered markers as important for subsequent analyses. Genetic diversity analyses were performed on the remaining 308,307 SNPs shared by all four breeds.</p>
Raw Data for the article: High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma
<p>Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.</p>
High Resolution Phragmites Australis Classification in Delaware Estuaries
<p>This dataset provides a high resolution (1-m) land cover map for Estuarine wetlands in the State of Delaware in the United States of America during the summer of 2017. This dataset was created to identify populations of the invasive marsh species <em>Phragmites australis</em>.</p> <p><strong>Input data:</strong></p> <p>This classification is derived from National Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in the State of Delaware during June of 2017. NAIP imagery includes a blue, green, red, and near infrared band. To improve classification accuracy, a Normalized Difference Vegetation Index (NDVI) was calculated from NAIP imagery using the near infrared and red bands. A principal component analysis (PCA) was used on the four NAIP bands and the NDVI band to create five new PCA bands. The five PCA bands were used as input into a random forest classification.</p> <p>NDVI = (Near infrared - Red) / (Near infrared + Red)</p> <p><strong>Classification methods:</strong></p> <p>We classified the input data using a Random Forest classifier with 100 trees. Data was classified into three coded land cover classes:</p> <p>1 - Phragmites</p> <p>2 - Other Vegetation</p> <p>3 - Open Water</p> <p>1,050 land cover reference points were collected with 70% used to train and 30% to test the classifier.</p> <p><strong>Accuracy:</strong></p> <p>Measures of accuracy including overall accuracy and per class user’s (UA) and producer’s accuracy (PA) of the random forest classifier were calculated.</p> <p>Overall accuracy: 95%</p> <p>Kappa: .92</p> <p>Phragmites: UA = 97% PA = 95%</p> <p>Other vegetation: UA = 92% PA = 96%</p> <p>Open water: UA = 100% PA = 95%</p> <p><strong>Code link:</strong></p> <p>The Google Earth Engine code used in this analysis is publicly available.</p> <p>https://github.com/mattswalter/Phragmites_Classification</p> <p><strong>Data for download:</strong></p> <p>The following zipped file is available for download:</p> <p> 1. Delaware_Phragmites_Classification.zip</p> <p>Contains a GEOTIFF titled "Phrag_DE_5PC" with the classified image for 2017. </p>
Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"
<p>The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making.</p> <p>All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow.</p> <p>The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a></p> <p>The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a></p> <p>To obtain maximum compression of the data, the tar command used to generate this tarball was:</p> <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre> <p> </p>
Figure 7 in Supported ionic liquid phase facilitated catalysis with lipase from Aspergillus oryzae for enhance enantiomeric resolution of racemic ibuprofen - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Figure 7. presents the scheme of ionic liquid immobilization on the silica surface. The file relates to the composite material manufactured for the NCN project OPUS, grant no. 2020/37/B/ST8/00693.</p>
High-resolution spatial multi-omics datasets
<p>Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu).</p> <p>These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments.</p> <p>DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location.</p> <p>RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations.</p> <p>Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets.</p> <p>Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting.</p> <p>Please find the uploaded readme.txt file for more details.</p>
Resolution of structural variation in diverse mouse genomes reveals chromatin remodeling due to transposable elements
<p>Structural variant calls, RepeatMasker annotations, and genome assemblies of diverse mouse genomes. </p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L2)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L1)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L3)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L4)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. </p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Resolutions of Local Maidans, November 2013 - February 2014
<p>This is a collection of local Maidans' resolutions – primary documents composed and voted by local Maidan assemblies from November 2013 through February 2014. Local Maidan resolutions are an important source of information about the dynamics of local protests and the contents of the protesters' claims during Ukraine’s 2013-14 Euromaidan social movement. The data are presented as an MS Excel document detailing the date, location (locality and oblast), actual text and source of a local Maidan resolution.</p> <p><strong>Background</strong></p> <p>In 2013-14, residents in all regions of Ukraine gathered for local protest rallies, called Maidans, people’s “<em>viches”</em> and meetings (jointly referred to as “local Maidans”), and issued demands towards local and national authorities. To add credibility to local Maidans' decisions, to disseminate information on protest and foster mobilization, these local assemblies had often put their claims in writing by issuing an assembly resolution and voting for its text. Resolutions were picked up and published by individual users of social media, local offices of public organizations and political parties, and local media. Using this and other outreach strategies, local Maidan communities around the country attempted to pressure local authorities, weaken the regime’s control and push the government into concessions over national and local issues.</p> <p><strong>Data Collection Methodology</strong></p> <p>I collected data on local Maidans' resolutions during 2014-15 by addressing the members of protest assemblies and organizations, contacting Ukrainian archives and museums, and by a complex Internet search algorithm that I designed.</p> <p>In particular, during 2013-14 local Maidans held All-Ukrainian Forums of Euromaidans to share experiences and coordinate efforts. I contacted the organizing committee and asked them to distribute my call for the collection of the original documents produced by local Maidans among Forums’ participants.</p> <p>I also contacted institutions in Ukraine that work on preserving Maidan history. Among these are the Lviv Oblast Archive, which presented an exhibit of documents on the Euromaidan movement in Lviv oblast, and the Ukrainian Institute of National Memory, which launched the Maidan archive in cooperation with the CSO “Maidan Museum.”</p> <p>I performed an Internet search using the following advanced Google search algorithms in Ukrainian and Russian:</p> <ul> <li><em>(viche OR meeting) AND (resolution OR demand OR statement) AND (city OR rayon OR oblast);</em></li> <li><em>Regional Maidan AND resolution AND (viche OR meeting);</em></li> <li><em>(Maidan or Euromaidan) AND resolution AND (viche OR meeting).</em></li> </ul> <p>In some cases, the text I found was titled e.g. “the resolution of the <em>second</em> city viche” (italics added), which suggested there might be several texts produced in the same locality. In such cases, I browsed the websites which published the initial text for the keywords of the algorithm. Additionally, I devised a location-focused search algorithm (in either Ukrainian or Russian, depending on the language of the initially-found text). For example, <em>[(viche OR protest) AND (resolution OR demands OR address) AND Chernivtsy] or [Chernivtsy AND resolution AND (second OR third) AND city viche].</em></p> <p>I also searched for videos of adopting Maidans' resolutions on YouTube – the biggest global source of video content, using the following algorithms, in Ukrainian and Russian:</p> <ul> <li><em>Maidan resolution;</em></li> <li><em>Euromaidan resolution;</em></li> <li><em>Resolution of people’s viche.</em></li> </ul> <p><strong>Data Cleaning</strong></p> <p>I applied the following filters to the documents I collected:</p> <ol> <li>the text had to reflect the meeting of a local community, and thus decisions of local and regional authorities who claimed to speak “on behalf of local people” were separated from this collection;</li> <li>the text of a resolution had to be presented in full -- I disregarded newspaper interpretations and excerpts; and</li> <li>a source had to have a clear indication of both the date and the place of the event where a resolution was adopted.</li> </ol> <p><strong>Data Characteristics</strong></p> <p>For November 2013 to February 2014, I found 94 resolutions that were adopted by 57 localities in 20 Ukraine's oblasts. Among these 57 localities, 31 are cities; of these 15 are oblast centers. The remaining 26 resolutions were adopted by the communities representing rayons, which are composed of towns and villages. Localities are quite diverse in size, ranging from 1.4 million residents (Kharkiv, Kharkiv oblast) to only 20,000 residents (Truskavets, Lviv oblast). On average, localities that adopted a Maidan resolution had below 200,000 residents; the median locality had 70,000 residents.</p> <p>The earliest resolutions date back to November 24, 2013. The most recent one was voted on February 27, 2014. The minimum number of resolutions adopted by a locality in this collection is one and the maximum is five.</p> <p>The collection is composed of 94 resolutions, declarations, addresses and decisions of local Maidans, which are jointly referred to as <em>resolutions</em>. A typical resolution is a one-page document, containing statements of protesters’ self-identification, the reasons for the protest, the demands to national and local authorities, calls for mobilization, and declarations of support or condemnation. Most of the local Maidan resolutions are in Ukrainian, and six texts are in Russian. It is important to note that by <em>texts </em>I refer mostly to the content of <em>text</em> <em>documents</em>. There were, however, few exceptions when I found the links to <em>video</em> <em>or graphic </em>evidence of the resolution proclamation.</p> <p>About half of the resolutions (55 texts out of 94) come from local media and include those published on local media outlets’ web pages and YouTube channels. These include 34 newspaper articles - usually short informative pieces, providing an introduction, some background information on the protests, a resolution text in full, and sometimes brief interviews with the speakers and the participants. In 18 cases, only a resolution text was published. In three cases I only have the videos of resolution adoption, usually accompanied by background information. Out of 55 cases, 25 news posts had no reference to the author at all, 18 were marked as “editorial”, and the remaining 12 news posts indicated the name of the journalist.</p> <p>The sources of the texts varied. Twelve texts were found on the web pages of local authorities (rayon and city councils, rayon state administrations). The other sources include CSOs' web pages, social media channels and blogs (12 texts), and local branches of political parties (five texts). Ten texts were posted by individual protesters on their private social media profiles (Facebook, personal YouTube channels, and blogs).</p> <p><strong>Potential Data Limitations</strong></p> <p>The local Maidan data is not a sample, but rather an attempt to collect the whole of the phenomenon, albeit within specific parameters. I attempted a comprehensive collection of local Maidan resolutions within a particular definition of local Maidan. I also limited the collection to a particular time. During Euromaidan not every locality had a protest and not every protest group chose to adopt a resolution. In some instances, the information about both the protest and the resolution can be found, but the text of the actual document remains out of reach. Yet, there may be some localities, which had both the protest and the resolution, that are not in my collection. These considerations illustrate the complexity of Ukrainian Euromaidan – a spontaneous, grassroots movement without open central coordination and record-keeping, and highlight that the exact universe of local Maidans cannot be fully assessed. Acknowledging this complexity, this collection is likely the largest and most complete of its kind.</p>
High spatial resolution satellite images for glacier outlines
<p>Two RapidEye satellite images acquired on Sep 13, 2013 and Sep 17, 2018. The two satellite images have a high spatial resolution of 5 m × 5 m and were used to derive the outlines of the Parlung No. 94 Glacier in years 2013 and 2018, respectively. The GaoFen-7 (GF-7) satellite image with a high spatial resolution of 3 m × 3 m acquired on Feb 26, 2021 was used to derive the outlines of the Dongkemadi Glacier in 2021</p>
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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.