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16,668 results for “AM-1”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
NECCPB-1: The first cropland parcel boundary dataset from meter-level imagery of Northeast China
<p>The Northeast China Plain is one of the world's three largest black soil regions, characterized by high organic matter content, rich nutrients, and strong water retention capabilities. Suitable climate conditions and abundant rainfall promote the growth of crops such as corn, soybeans, and rice, making it one of the main grain production bases in China, accounting for about one-fifth of the country's grain output. The grain production in the Northeast China black soil region is crucial for food security in China and globally. This area's farmland parcels are the basic units of agricultural production and the cornerstone of precision agriculture management, providing detailed information on cultivated land location, boundaries, shape, and area. Utilizing this parcel-scale information, governments and farm managers can devise more precise planting strategies and optimize management methods, thereby enhancing the quality and productivity of crops, ensuring a continuous food supply, and promoting sustainable agricultural development.</p> <p>The first cropland parcel boundary dataset from meter-level imagery of Northeast China (NECCPB-1) was developed based on deep learning models and a custom-designed automatic parcel merging strategy. A total of 10.22 TB of very-high-resolution (VHR) imagery was downloaded and uploaded, covering the entire region of Northeast China and an area of 1,240,000 km². After further removal of non-cropland regions based on phenological differences, 32,395,946 parcels were obtained.</p> <p> Rigorous validation using manually drawn reference parcels demonstrated that this dataset had high accuracy in parcel delineation (Extraction Precision, EP: 0.85) and high consistency with the reference parcels (|Completeness Deviation|, |CompD|: 0.02; Intersection over Union, IoU: 0.90). Further comparison with official Third Survey reports confirmed the high reliability of the NECCPB-1 dataset, which exhibited an average relative difference of -3.6% and an absolute relative difference of 9.8%.</p> <p>A series of cross-validations with seven widely used cropland datasets (ESA_GLC10, ESRI_GLC10, FROM_GLC10, CLD10, GLAD250, GFSAD30, and SinoLC-1). The recall, precision, and F1 scores of the NECCPB-1 were calculated as 0.91, 0.93, and 0.92, respectively, using publicly validated sample points of land cover. Moreover, NECCPB-1 performed best regarding cropland completeness, achieving an intersection ratio (IR) of 0.94, as calculated using the reference parcels.</p> <p>Due to the extensive size of the dataset and potential policy considerations, access to the data will be granted based on specific inquiries. Please contact us at zhengjia@iga.ac.cn or guotianhao@iga.ac.cn for further details. Please indicate your purpose and other details. Thank you!</p>
InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR
<p>A stack of unwrapped interferograms in the San Francisco Bay area, California, USA</p> <p>Sensor: Sentinel-1 descending track 42</p> <p>Processor: <a href="https://github.com/gmtsar/gmtsar" target="_blank" rel="noopener">GMTSAR</a></p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p>The tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p><strong>Version 1.x (~2.3 GB)</strong><br>Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)</p> <p><strong>Version 0.x (~290 MB; for fast testing of code development)</strong><br>Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)</p>
Concatenated Data from the Chang'E-1 and -2 Microwave Radiometers
<p>This dataset includes a binary table collecting all data released by NAOC from the Chang'E 1 and 2 Microwave Radiometers, as well as gridded, map-projected versions of those data. Please see readme.md for a detailed description of contents.</p>
Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1
<p>Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1, FRM-II dataset. Intended for Machine learning purposes. Data generated by performing simulations in <a href="https://www.mcstas.org/">McStas</a> with the <a href="https://www.sasview.org/docs/user/qtgui/Perspectives/Fitting/models/index.html">SasView small angle scattering form factor models</a> describing the sample interaction. Two parameter spaces are varied sistematically: form factor model parameters and instrument configuration parameters. For more detailed information, read the <code>README.md</code> file of this database.</p> <p>The database contains 46 SANS form factor models under different instrument configurations. All data is uploaded in <code>hdf5</code> files, and the corresponding metadata in <code>.csv</code> files. Description of what each instrument configuration means (sample-detector distance, collimation, incident wavelength) and which model is used is contained in the metadata file. </p> <p>Each array is the result of the position sensitive detector output in neutron intensity (float values). A Dataset loader for Pytorch may be found <a href="https://github.com/jorobledo/hdf_loader_pytorch" target="_blank" rel="noopener">in GitHub</a> and is intended for Machine Learning purposes.</p>
Supporting Data - Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet
<p>This dataset contains supporting data accompanying Culberg, R., Michaelides, R. J., and Miller, J. Z.: Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet, EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2023-2652">https://doi.org/10.5194/egusphere-2023-2652</a>, 2023. The final accepted manuscript will be linked via the same preprint server at the time of publication. The dataset contains the following files:</p> <ul> <li>Sentinel-1 HV and HV/HH backscatter mosaics of the Greenland Ice Sheet formed using data from 1 Oct 2016 - 30 April 2017.</li> <li>Estimated average annual summer melt extent between 1 Nov 2014 and 31 Aug 2020, detected using seasonal variations in Sentinel-1 HH backscatter.</li> <li>The firn aquifer extent over Greenland derived from Sentinel-1 in Brangers et al. (2020), reprojected to EPSG:3413.</li> <li>The ice mask used in the study, derived from the BedMachine Greenland ice mask.</li> <li>The training and validation datasets derived from the Jullien et al. (2023) ice slabs detections from ice penetrating radar data that were used to optimize ice slab detection thresholds for the Sentinel-1 backscatter mosaics. </li> </ul>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
DRM-labeled HIV-1 protease sequence dataset
<p>An HIV-1 protease dataset with labeled DRMs derived from the Stanford HIV Drug Resistance Database<sup>1,2</sup> is provided. It is in <em>fasta</em> format with major protease drug resistance mutations (as defined by Wensing et al.<sup>3</sup>) provided in the sequence name section (following the ">" symbol) as a comma-separated list. The dataset was used in the following paper: "Ahmed A., de Souza D. R., Link R. W., Nonnemacher M. R., Wigdahl B., Dampier W. Design of a SHERLOCK-based low resource screening assay for HIV-1 drug resistance, in preparation, 2021. "</p>
Modelled and Sentinel-1 detected firn aquifers areas in the Antarctic Peninsula
<p>FDM results: This dataset contains firn aquifer extent output from IMAU-FDM (Firn Densification Model), version v1.2A, for the Antarctic Peninsula on a 5.5 km grid. The dataset consists of maps of the extent of simulated seasonal aquifers in at least one year (2017-2020), perennial aquifers in at least on year (2017-2020), and perennial aquifers in all years (2018-2020). Further details are described in Buth et al. (2022).<br> Model adjustments and run were performed by Sanne B. M. Velduijsen.</p> <p>S1 detection results: The GeoTIFF image is the result of the Sentinel-1 firn aquifer detection routine which makes use of the typical delayed increase of SAR backscatter after the peak melt season in case of an aquifer. The image has two bands per year (2017-2020), one containing the DOY80 parameter for the whole Antarctic Peninsula (excluding masked areas, see Buth et al, 2022), the other containing DOY80 only for detected aquifer areas, where it exceeds the threshold of DOY80=105. DOY80 here stands for the day of the year at which 80% of the September Sentinel-1 HH backscatter is reached. Further details are described in Buth et al. (2022).<br> S1 aquifer detection was performed by Lena G. Buth, using the Python API of the Google Earth Engine. The associated code is available as a GitLab project: https://gitlab.awi.de/lenbuth/tc-aquifers</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
RNA sequencing data for bleomycin exposed THP-1 macrophages
<p>This dataset contains normalized counts matrices, from dds_deseq objects, from DeSeq2 analysis of RNA sequencing data, from THP-1 macrophages exposed to multiple doses of bleomycin in the range of 0-100µg/ml for 24H, 48H or 72H.</p>
Sentinel-1 data stack for Masjed Soleyman Dam
<p>This is a Sentinel-1 sample dataset for the SARvey InSAR time series analysis software.</p> <p>This dataset consists of:</p> <ul> <li> A stack of coregistered SLCs for the Masjed Soleyman Dam and its corresponding geometry data in MiaplPy format. These files serve as the input data for SARvey.<br> SARvey_input_data_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> <li> The final products generated by SARvey for reference.<br> SARvey_final_results_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> </ul> <p><br>A cookbook is available to help you run the software using this dataset.</p> <p> </p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the southern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the northern HOME tomography array.</p>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Change in total simulated seafloor biomass between the late Scenario period (2081-2100) and late Historical period (1995-2014) under the SSP scenarios 126 to 585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a.</p> <p> </p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Annual mean seafloor biomass for periods 1980 to 2014 (Historical) and 2015 to 2100 (Future) for Shared Socioeconomic Pathways SSP126 to SSP585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a. Each file contains seafloor detritus, total seafloor biomass and seafloor biomass for each of BORIS-1's 16 size classes.</p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.
<p>An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018). </p> <p>The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature. </p> <p>The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here:</p> <p><product_type>_<mission>_<mode>_<product>_<polarisation>_<starttime>_<endtime>_<orbitnumber>_<dataID>_<image>.fileextension</p> <p>For example:</p> <p>extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif</p>
Single-molecule DNA methylation patterns of full-length human-specific LINE-1 (L1HS) retrotransposons in a panel of cell lines.
<p>We used bs-ATLAS-seq to comprehensively map the genomic location and assess the DNA methylation status of full-length human-specific LINE-1 elements (L1HS). The approach capture region 1-210 of L1HS elements, which corresponds to the most 5' end of its promoter sequence. This was performed in a panel of 12 human primary or transformed cell lines (BJ, IMR90, MRC5, H1, K562, HCT116, HeLa S3, HepG2, MCF7, HEK-293, HEK-293T, 2102Ep), many being shared with the encode project.</p> <p>These datasets provide a visualization for DNA methylation patterns at the single molecule level for each L1HS loci.</p>
NJR-1 Dataset
<p>NJR is a Normalized Java Resource. The <em>NJR-1</em> dataset consists of 293 Java programs that can be used with several analysis tools.</p> <p> </p> <p><strong>TOOLS THAT RUN ON NJR-1</strong></p> <p>Each program runs successfully with the following 14 Java static analysis tools:</p> <ol> <li>SpotBugs (https://spotbugs.github.io)</li> <li>Wala (https://wala.github.io)</li> <li>Doop (https://bitbucket.org/yanniss/doop)</li> <li>Soot (https://github.com/soot-oss/soot)</li> <li>Petablox (https://github.com/petablox/petablox)</li> <li>Infer (https://fbinfer.com)</li> <li>Error-Prone (http://errorprone.info)</li> <li>Checker-Framework (https://checkerframework.org)</li> <li>Opium (Opal-framework) (https://www.opal-project.de)</li> <li>Spoon (https://spoon.gforge.inria.fr)</li> <li>PMD (https://pmd.github.io)</li> <li>CheckStyle (https://checkstyle.org)</li> <li>JavaParser (https://javaparser.org/)</li> <li>Codeguru* (https://aws.amazon.com/codeguru)</li> </ol> <p>In addition to these static analysis tools, the NJR dataset has also been tested with 9 other tools that operate on Java bytecode.</p> <ol> <li>Jacoco (https://www.jacoco.org): Dynamic analysis tool</li> <li>Wiretap (https://github.com/ucla-pls/wiretap): Dynamic analysis tool</li> <li>JReduce (https://github.com/ucla-pls/jreduce): Bytecode reduction tool</li> <li>QueryMax (https://doi.org/10.5281/zenodo.5551128): Preprocessor for application code analysis</li> <li>Call-Graph Pruner (https://doi.org/10.5281/zenodo.5177161): Static call-graph pruning tool</li> <li>FootPatch (https://github.com/squaresLab/footpatch): Automated Repair Tool</li> <li>Procyon (https://github.com/ststeiger/procyon): Decompiler</li> <li>CFR (https://www.benf.org/other/cfr/): Decompiler</li> <li>Fernflower (https://github.com/fesh0r/fernflower): Decompiler</li> </ol> <p> </p> <p><strong>BENCHMARK PROGRAMS</strong></p> <p>The NJR programs are repositories picked from a set of Java-8 projects on Github that compile and run successfully. Each of these programs comes with a jar file, the compiled bytecode files, compiled library files, and the Java source code. The availability of the files in both jar-file form, as well as source code form (with the compiled library classes) is a major reason the dataset works with so many tools, without requiring any extra effort. These features make NJR-1 a great benchmark for any kind of Java static analysis, dynamic analysis, or tool building.</p> <p>Internally, each benchmark program has the following structure:</p> <ul> <li><em>src:</em> directory with source files.</li> <li><em>classes:</em> directory with class files.</li> <li><em>lib:</em> compiled third party library classes (source files not available, since libraries are distributed as class-files).</li> <li><em>jarfile:</em> jar file containing the compiled application classes and third-party library classes.</li> <li><em>info:</em> directory with information about the program. It includes the following files. <ul> <li><em>classes:</em> list of application classes (excludes third-party library classes).</li> <li><em>mainclasses:</em> list of main classes that can be run.</li> <li><em>sources:</em> list of source file names.</li> <li><em>declarations:</em> list of method declarations categorized by source file name.</li> </ul> </li> </ul> <p>The benchmarks already come with a compiled JAR file, but some tools need to compile and run the benchmarks. The following simple commands can be used for compilation and running (replace <jarfilename> with the file in the <em>jarfile</em> directory. replace <mainclassname> with any of the classes from info/mainclasses):</p> <p><em>javac -d compiled_classes -cp lib @info/sources</em></p> <p><em>java -cp jarfile/<jarfilename> <mainclassname></em></p> <p> </p> <p><strong>FILES AVAILABLE FOR DOWNLOAD</strong></p> <p>There are 4 files available for download: <em>njr-1_dataset.zip, scripts.zip, benchmark_stats.zip, and a Readme.</em></p> <p><em>njr-1_dataset.zip</em> has the actual dataset programs. <em>scripts.zip</em> contains Python3 scripts for each tool, to run it on the entire dataset. The Readme details the version number, download link and setup instructions for each tool. The <em>benchmark_stats.zip </em>file lists some statistics for the benchmark programs.</p> <p> </p> <p><strong>STATISTICS</strong></p> <p>Here are some summary statistics about the benchmark programs:</p> <ul> <li>The mean number of application classes: 97</li> <li>Each program executes at least 100 unique application methods at runtime.</li> <li>The mean lines of application source code: 9911</li> <li>The mean number of 3rd party library classes: 2608</li> <li>The mean (estimated) lines of 3rd party library source code: 250,000</li> <li>The mean number of static edges in the application call graph: 1404</li> <li>The mean number of dynamic edges in the application call graph: 469 </li> </ul> <p> </p> <p><strong>NOTES</strong></p> <p>Note: Zenodo shows some changes for this repository. However, all the changes involve updating the scripts folder, as more tools get tested on the dataset. The programs in the dataset themselves remain unchanged.</p> <p>*Note 2: Codeguru Reviewer is a paid, proprietary tool by Amazon. Our experiments show that it runs successfully on all the benchmarks in this dataset. However, we don't include any scripts to replicate this run because of its paid nature.</p> <p>To cite this dataset, please cite the following paper:<br> Jens Palsberg and Cristina V. Lopes, NJR: a Normalized Java Resource. <br> In Proceedings of ACM SIGPLAN International Workshop on State Of the Art in Program Analysis (SOAP), 2018.</p>
Arctic sea ice radar freeboard from ERS-1, ERS-2, Envisat and CryoSat-2
<p>This dataset presents a radar freeboard time series from 1993 to 2021 for Arctic sea ice. Envisat, ERS-2 and ERS-1 radar freeboards have been estimated using CryoSat-2 as a reference, they are "SAR-like" estimations as they have been calibrated on CS-2 SAR TFMRA50 radar freeboard. </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.