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7,195 results for “SET”
Secretin-like class B G protein-coupled receptor (GPCR) mutation data set
<p>Curated set of 2463 quantitative mutation data points covering 13 secretin-like class B G protein-coupled receptors.</p> <p>Annotated according to GPCRdb standards (http://gpcrdb.org/), complemented by GPCRdb Ballesteros-Weinstein numbers assigned using GPCRdb KNIME nodes (https://github.com/3D-e-Chem/knime-gpcrdb).</p> <p>The data set is build from data sets published in:</p> <p>- Siu et al. Nature 2013, 499: 444-449. doi:10.1038/nature12393</p> <p>- Hollenstein, de Graaf et al. Tr Pharmacol Sci 2014, 35: 12-22. doi: 10.1016/j.tips.2013.11.001</p> <p>- Yang, de Graaf et al. J Biol Chem 2016, 291: 12991-3004. doi: 10.1074/jbc.M116.721977</p>
Data set published in the IEEE TCAD article "Custom Multi-Cache Architectures for Heap-Manipulating Programs"
<p>This data set contains the results presented in the paper "Custom Multi-Cache Architectures for Heap-Manipulating Programs", published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) in 2016.</p> <p>The data set consists of two parts, a Microsoft Excel file ('FPGA_implementation_results.xlsx') and a Matlab script ('plot_cache_performance.m', in combination with measurement results in an ascii file).</p> <p>The Excel file contains<br /> - the FPGA resource utilisation,<br /> - execution time measurements,<br /> - hit rate measurement of the multi-cache system,<br /> - and power measurements</p> <p>of different FPGA designs with different on-chip cache configurations. The resource utilisation is split into FPGA slices, LUTs, FlipFlops, DSP slices and block RAMs. Results in this file can be found in Table I-IV in the paper. Please refer to the paper for more information or email f.winterstein12@imperial.ac.uk.</p> <p>The Matlab script loads a data file ('cache_performance_N16384_L1') containing the hit rate measurements for different cache sizes of two direct-mapped cache with 64bit line width. The script produces a 3D 'skyscraper' plot, i.e. a grid of coloured bars. Each bar corresponds to the hit rate measured at the particular cache size configuration. The plot is saved in the file 'surf.pdf'. The script was used to produce Figure 4 of the paper. Please refer to the paper for more information or email f.winterstein12@imperial.ac.uk.</p> <p>In addition to this description, we include an author copy of the paper. Note that this is not the official version of the paper. Please cite the original IEEE TCAD article if you use the data.</p>
LIBER Academic Librarians RDS Data Set
<p>Included are the data set used in the LIBER/Second Academic Libraries Follow-Up Assessment, the survey instrument, and an about file are available.</p>
Imperial College London femur and tibia surface mesh set
<p>The dataset contains bone geometries of the left and right thigh (femur) and shank (tibia and fibula) segmented from magnetic resonance (MR) scans of 35 healthy volunteers (22 male, 13 female; height 155 cm to 193 cm, weight 45 kg to 108 kg, body mass index 17.0 kg/m<sup>2 </sup>to 34.8 kg/m<sup>2</sup>).</p> <p>If you are using this dataset, please cite:</p> <p>Nolte, D., Tsang, C.K., Zhang, K., Ding, Z., Kedgley, A.E., Bull, A.M.J., 2016. Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models. J. Biomech. 49, 3576–3581. doi:10.1016/j.jbiomech.2016.09.005</p>
MPI load balancing simulation data sets (companion to IPDPS 2017)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to IPDPS 2017, under the title "Using Simulation to Evaluate and Tune the Performance of Dynamic Load Balancing of an Over-decomposed Geophysics Application".</p> <p>The following contents are included:</p> <ul> <li><em>IPDPS2017.org :</em> Org mode (Emacs) file containing the shell (Bash) and R scripts used to: <ul> <li>run the load balancing simulation;</li> <li>process the traces of both real executions (Tau traces) and simulation (Pajé traces);</li> <li>generate the graphics.</li> </ul> </li> <li><em>lb_traces/:</em> this directory contains the raw traces from real executions and SMPI emulations of the Ondes3D application.</li> <li><em>processed_data/</em>: this directory contains the results of the processing of the traces in the form of CSV format data files which are be used to generate the graphics.</li> <li>i<em>mg</em>/: this directory contains the generate graphics, in PNG format.</li> </ul> <p> </p>
Validation data set for automatic blood vessel segmentation in colorectal cancer histology (IHC)
<p><strong>Content</strong></p> <p>This data set contains 100 histological image patches of 1000 * 1000 px size. The samples were immunostained for CD34 (3,3'-Diaminobenzidine, DAB [brown]) with hematoxylin (blue) counterstain.</p> <p>Furthermore, the data set contains a table of blood vessel counts in each image by three blinded observers as well as an automatic count with a method based on the following paper:</p> <p>Kather, Jakob Nikolas et al. "Continuous Representation Of Tumor Microvessel Density And Detection Of Angiogenic Hotspots In Histological Whole-Slide Images". <em>Oncotarget</em> 6.22 (2015): 19163-19176. http://dx.doi.org/10.18632/oncotarget.4383</p> <p><strong>Image format</strong></p> <p>All images are RGB, 0.50 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors and liver metastases) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).</p> <p><strong>Ethics statement</strong></p> <p>All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the Declaration of Helsinki.</p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br> Dr. Jakob Nikolas Kather<br> http://orcid.org/0000-0002-3730-5348<br> ResearcherID: D-4279-2015</p>
16S rRNA sequences data set of Dicronocephalus species in this study.
Explanation note: This 16S rRNA data includes 46 individual sequences of the examined Dicronocephalus species in this study.
Magnesium AZ31 HRDIC Data Set
<p>This pack contains the data set obtained from the high-resolution digital image correlation (HRDIC) study of an AZ31 magnesium alloy. This data set can be visualised using the notebook (https://doi.org/10.5281/zenodo.376503), which can be used as a companion to out article entitled " Why magnesium is not brittle: a quantitative study on the accommodation of deformation incompatibility". Additionally, EBSD data set for the same region can be found in https://doi.org/10.5281/zenodo.345925.</p> <p>This data repository contains:</p> <ul> <li>AZ31.txt: correlation data.</li> <li>AZ31.npy: correlation data in binary iPython format.</li> <li>MSSPowerNorm.tif: high-resolution image of the effective shear strain using the data set and the iPython notebook.</li> </ul> <p>Patterning was performed using an in house styrene-assisted gold remodelling device producing gold speckles sizes in the range 20-40 nm.</p> <p>Backscattered electron images were obtained using a FEI Magellan HR 400L FE-SEM at a working distance of 3.2 mm, 1 kV, 0.8 nA beam current, using immersion mode and a beam deceleration with stage bias of 2 kV. Mosaics of 15x15 images were used to cover 135x120 µm<sup>2</sup>. Each tile is an image containing 2048 x 1768 pixels and has a horizontal field of view of 14.92 µm. The images were overlapped by 25% to enable easy stitching prior to the digital image correlation. Two mosaics were obtained, one before and one after deformation</p> <p> </p>
Eriksson et al, ICAPS 2017: Data set from experiments
<p>This is the experiment data used in the paper "Unsolvability Certificates for Classical Planning" by Eriksson et al. (ICAPS 2017). The file "properties" contains all parsed attributes from each experiment run in json-format, while the file "Report_icaps17.html" containes an overview of the most important attributes in html-format.</p>
A modular set of synthetic spectral energy distributions for young stellar objects - Robitaille (2017) - v1.1 [Hyperion files]
<p>These are the input and output files for the radiative transfer code (Hyperion) for the model sets presented in</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>Each model set is provided as a single tar file. Each tar file expands to <strong>grids-1.1/<set name></strong>, so if you expand all tar files in the same folder, you will end up with a single <strong>grids-1.1</strong> folder with 18 sub-folders, one for each model set.</p> <p>For a given model set, the files are as follows:</p> <ul> <li>grids-1.1/<set name>/input - input Hyperion files</li> <li>grids-1.1/<set name>/log - log files from Hyperion</li> <li>grids-1.1/<set name>/output - output Hyperion files</li> <li>grids-1.1/<set name>/par - parameters for each model</li> <li>grids-1.1/<set name>/ranges.conf - ranges of parameters varied in the model set</li> <li>grids-1.1/<set name>/parameters.hdf5 - table of parameters for all models</li> <li>grids-1.1/<set name>/d03_5.5_3.0_A_sub.hdf5 - dust file used for the models</li> </ul> <p>Given the large number of models for some of the model sets, the models are not all stored directly inside the par, input, output or log directories - instead these directories contain folders formed from the first two characters (forced to lowercase) of the names of the models they contain. For example, a3 contains all models whose name starts with a3 or A3. This was done to avoid having too many files in a single folder which can cause issues on certain file systems.</p> <p>For the Hyperion input and output files, in some cases an _sed file is present. In these cases, the output SEDs (and polarization spectra) should be read from the _sed file, not the original output file. This is the case for all models that are in a set for which the ambient medium was present, as described in §4.2.3 of Robitaille (2017). Furthermore, in some cases the SED file is called _sed_noscat to indicate that scattering was not included, as described in §5.1 of Robitaille (2017).</p> <p>To avoid taking up too much disk space, the Hyperion HDF5 input/output files use external links to refer to each other and to the dust file. To make sure the links work, you should do all operations with the input/output files from the directory containing <strong>grids-1.1</strong>. For example, to open a Hyperion output file, you would need to do (in Python):</p> <p> In [1]: from hyperion.model import ModelOutput</p> <p> In [2]: mo = ModelOutput('grids-1.1/s---s-i/output/a3/A3kQmQtj.rtout')</p> <p>A notebook with examples of reading in the output files can be found here:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/blob/master/notebook_raw/reading_raw_files.ipynb</p> <p>More information on using Hyperion, including reading input/output files, can also be found at http://docs.hyperion-rt.org</p> <p>For <strong>announcements</strong> of new versions of these models, you can subscribe to the following mailing list:</p> <p>https://groups.google.com/forum/#!forum/protostars</p> <p>For <strong>questions or issues</strong> using these models, you can open a GitHub issue in the companion repository:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/issues/new</p>
Data set for Microscale and nanoscale strain mapping techniques applied to creep of rocks
<p>Data set (figures and data involved in their making) for Quintanilla-Terminel, A., M. E. Zimmerman, B. Evans, and D.L. Kohlstedt, Microscale and nanoscale strain mapping techniques applied to creep of rocks, Solid Earth Discuss., https://doi.org/10.5194/se-2017-27, in review, 2017.</p>
Added graph to previous Alsop data set
<p>A previous plot compared CHU 14.67 MHz Doppler shift between the day before and day of eclipse.</p> <p>I also had available Doppler shift data from 8/19 (two days before). I added that to the graph to perhaps help detect or dismiss a previously observed effect. The two days before the eclipse were pretty comparable from a Doppler Shift standpoint.</p>
Data set from ambient vibration monitoring and static loading of a steel stringer bridge subject to imposed damage
<p>This data set contains structural response measurements acquired from the Route 345 Bridge over Big Sucker Brook in Waddington, NY prior to demolition and replacement. This steel stringer bridge was instrumented with dual-axis accelerometers and strain transducers and response measurements were obtained prior to and following mechanically imposed damages. Accelerometer measurements were obtained under vehicle passes and strain measurements were acquired during static loading of the span with a truck positioned at three prescribed locations. All measurement data is contained in a single h5-file (hierarchical data format version 5). The data is shared with the intent of promoting the advancement of structural health monitoring and vibration-based damage detection. </p> <p><strong>Version 2 Note: </strong>This version corrects the strain measurement data. Due to an index counter error in the script that compiled the strain measurement data into the h5-file, the strain measurement data in the Version 1 data were incorrectly sourced from a single scenario. The strain measurements are corrected in this new version. No other changes were made in the h5-file.</p>
PhotIQA: A photoacoustic image data set with image quality ratings
<div>To support the development and testing of full- and no-reference IQA measures we assembled PhotIQA, a data set consisting of 1134 photoacoustic (PA) images reconstructed with 3 different algorithms and ratings by 2 experts across five quality properties (overall quality, edge visibility, homogeneity, inclusion and background intensity). To allow full-reference assessment, highly characterised imaging test objects were used, providing a ground truth. A detailed description of the data set can be found in [1], where also baseline experiments give first insights. </div> <div> </div> <div>We have decided to make the data set available to the research community under the Creative Commons Attribution 4.0 International license. If you use the data in your research, we kindly ask that you reference the repository and the corresponding paper</div> <div> </div> <div> <div>[1] A. Breger, J.Gröhl, C. Karner, T.R. Else, I. Selby, J. Weir-McCall, C.-B. Schönlieb: PhotIQA: A photoacoustic image data set with image quality ratings, preprint on arXiv: http://arxiv.org/abs/2507.03478, July 2025</div> <div> </div> <div>Update v2.0 (July 7): quality ratings of more properties are available now, including edge visibility, object homogeneity, intensity (background), intensity (inclusions), overall quality</div> <div> </div> <div>Update v2.1 (August 20): the z-scores of the raters have been added and updated for mos</div> </div>
quapps Instance Data Set
<h1>quapps Instance Database</h1> <p dir="auto">In the quapps package, concrete problem types for quantum optimization are specified. A detailed description of each of the problem types can be found in the corresponding Gitlab repository (<a href="https://gitlab.com/quantum-computing-software/quapps" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.com/quantum-computing-software/quapps</a>). We will therefore not explain the problems in more detail here.</p> <p dir="auto">The instantiated optimization problems of this database are based on the quapps package and were created with the implemented random generators. In view of the qubit numbers available in the near future, the number of instances was limited to instances that comprise a maximum of 128 variables. This resulted in different different combinations of the defining parameters, such as the size of the graphs or their density:</p> <ul> <li> <p>Maximum Cut:</p> <ul> <li>numbers of nodes 𝑁 ∈ {8, 16, 32, 64, 128, 175},</li> <li>densities 𝑑 ∈ {0.4, 0.6, 0.8} (too low densities lead to non-connected graphs, whereas graphs, whereas a density of 1.0 results in a complete graph for which the maximum cut problem for which the maximum cut problem is trivial), and</li> <li>a randomly chosen integer edge weighting between 1 and 5 or no weighting at all,</li> </ul> </li> <li> <p>Maximum Colorable Subgraph:</p> <ul> <li>node numbers 𝑁 ∈ {8, 12, 16},</li> <li>densities 𝑑 ∈ {0.4, 0.6, 0.8} and</li> <li>a number of colors from 3 to the maximum possible for the respective graph such that the number of variables does not exceed 175,</li> </ul> </li> <li> <p>Ising model:</p> <ul> <li>qubit numbers 𝑁 ∈ {8, 16, 32, 64, 128, 175} and</li> <li>coupling densities 𝑑 ∈ {0.2, 0.4, 0.6, 0.8, 1.0}</li> <li>with an accuracy of 2 decimal places,</li> </ul> </li> <li> <p>Prime Factorization:</p> <ul> <li>two random prime numbers</li> <li>with a number of 3 to 11 bits per prime number</li> <li>which result in a non-trivial optimization problem,</li> </ul> </li> <li> <p>Traveling Salesperson:</p> <ul> <li>node numbers from 8 to 13.</li> </ul> </li> <li> <p>Graph Partitioning:</p> <ul> <li>number of nodes N ∈ {8, 15, 35} (limited since number of variables is product of N and k and shall not surpass 175)</li> <li>densities d ∈ {0.4, 0.6, 0.8} (densities can not be too small so a valid graph can be generated)</li> <li>number of subgraphs k ∈ {2, 3, 5} (chosen such that the highest number of variables is 35*5 = 175)</li> <li>weights chosen randomly (uniformly) between 0 and 10 with 2 decimal places</li> </ul> </li> <li> <p>Knapsack:</p> <ul> <li>number of items I ∈ {32, 100, 175} (cant go higher because number of variables = number of items)</li> <li>maximum value v ∈ {6, 12, 24}</li> <li>maximum weight w ∈ {6, 12, 24}</li> <li>maxim weight limit W ∈ {20, 40, 80} (range chosen in accordance with maximum weights)</li> </ul> </li> <li> <p>Minimum k-Union:</p> <ul> <li>number of elements E ∈ {40, 80, 120} (limited since number of variables = number of elements + number of subsets)</li> <li>number of subsets S ∈ {40, 45, 50} (chosen such that the highest number of variables is 125 + 50 = 175)</li> <li>choices of k ∈ {12, 16, 20} (must not exceed number of subsets but needs to be big enough so that at least one valid covering exists)</li> <li>biggest subset sizes s ∈ {28, 34, 40} (must be big enough to ensure existence of valid covering)</li> </ul> </li> <li> <p>Subset Sum:</p> <ul> <li>number of different numbers N ∈ {40, 80, 120, 175} (decides number of variables so limited to 175)</li> <li>maximum number M ∈ {30, 60, 150, 300} (chosen such that for all N, there exists one M thats smaller and one M thats bigger (to allow for both instances with duplicate numbers and instances with unique numbers))</li> <li>maximum target sum T ∈ {500, 5000, 10000} (big enough so that instances with low N and M can still find a valid sum)</li> </ul> </li> </ul> <p dir="auto">We created 5 different instances for each parameter configuration. This results in a total of 125 Ising, 244 Maximum Colorable Subgraph, 150 Maximum Cut, 124 Prime Factorization, 25 Traveling Salesperson instances, 135 Graph Partitioning, 405 Knapsack, 405 Minimum k-Union and 240 Subset Sum Instances.</p> <p dir="auto">Additionally we added 96 Flight-Gate Assignment instances from our publication (<a href="https://doi.org/10.1007/978-3-030-14082-3_9" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1007/978-3-030-14082-3_9</a>):</p> <ul> <li>Flight-Gate Assignment: <ul> <li>with 3 to 17 flights and</li> <li>correspondingly 3 to a maximum of 17 gates.</li> </ul> </li> </ul> <pre> </pre>
Data sets and R codes for "It's about her: male within-season movements are related to mate searching in a songbird"
<p><strong>Abstract</strong></p><p>In species with resource-defense mating systems (such as most temperate-breeding songbirds), male dispersal is often considered to be limited in both frequency and spatial extent. When dispersal occurs within a breeding season, the favored explanation is ecological resource tracking. In contrast, movements of male birds associated with temporary emigration, such as polyterritoriality (i.e., defense of an additional location after attracting a female in the initial territory), are usually attributed to mate searching. We suggest that male dispersal and polyterritoriality are functionally related, and that mate searching may be a unifying hypothesis for predicting the within-season movements of male songbirds. Here, we test three key predictions derived from this hypothesis in Wood Warblers <i>Phylloscopus sibilatrix</i>. We collected data on the spatial behavior of 107 males between 2017 and 2019, and related male movements to a new territory (both in a dispersal and polyterritorial context) to mating potential in the current territory. Most males dispersed from their territories within days or weeks after failing to attract a female, despite occupying territories in apparently suitable habitat. Probability of polyterritoriality by paired males increased after the peak fertile period of their mate. Males never dispersed following nest predation if the female remained to renest. Thus, our data are consistent with the hypothesis that both movement types are functionally related to mate searching.</p>
Data set: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution". The data consist of:</p><p> </p><p><strong>ASCII files:</strong></p><ol><li><strong>SMB-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt</strong>: time series of Antarctic sector-integrated annual SMB<strong> (Gt per year)</strong> from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt (Gt per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1950-2014-RACMO2.3p2-CESM2-HIST-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution (1950-2014).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP126-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP245-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP585-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution (2015-2099).</li></ol><p>Antarctic sectors include the Antarctic Peninsula (APIS), the West Antarctic ice sheet (WAIS), the East Antarctic ice sheet (EAIS), the grounded Antarctic ice sheet (AIS), the floating ice shelves (Ice shelves), and the whole of Antarctica (ANT) including both the AIS and Ice shelves. The APIS, WAIS, EAIS and AIS sectors include land ice from neighbouring Antarctic islands.</p><p> </p><p><strong>Netcdf files:</strong></p><ol><li><strong>smb_rec.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual SMB (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1950-2014.BN_RACMO2.3p2_ANT27_CESM2_HIST.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution, covering the whole of Antarctica (1950-2014).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP126.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP245.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP585.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>ANT_masks.2km.nc</strong>: file including the grounded AIS mask (AIS), Antarctic sectors mask (Sectors), floating ice shelves mask (Shelves), surface elevation down-sampled from REMA (Topography), latitude and longitude on the 2 km grid<strong>. </strong>The sector mask includes: 0 – Ocean, 1 – APIS, 2 – WAIS, 3 – EAIS, 4 – APIS islands, 5 – WAIS islands, 6 – EAIS islands, and 7 – ice shelves.<strong> </strong></li></ol><p>The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 2 km x 2 km. </p><p> </p><p><strong>Additional data: </strong>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6, moderate SSP2-4.5 and high-end SSP5-8.5 warming scenario are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, drifting snow erosion, and total sublimation (surface and drifting snow) at 2 km horizontal resolution. </p><p> </p><p><strong>Abstract:</strong> Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.</p>
Data Sets: Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer
<p>Date of data analysis: Statistical analyses conducted throughout the 2023 year </p><p>Information about funding sources that supported the collection of the data:</p><p>The research was supported by the Cooperative Institute for Modeling the Earth System at Princeton University under Award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, and by the US National Science Foundation under award number AGS 2128345. Also, it was supported by the National Defense Science and Engineering Graduate Fellowship from the U.S. Department of Defense and Army Research Office. Similarly, the National Science Foundation provided support to complete the PHOXMELT field studies (Grant PLR- 1417914) to collect the data. Also, the study was supported by the U.S. National Science Foundation (NSF-AGS-2028633) and the Department of Energy (DE-SC0022072).</p><p>The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration.</p><p>This dataset contains the observational data for the two field experiments (Barrow and Wendell) in .nc file format.</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
Simulated data sets with different SNRs and NCEs for PIPI2
<p>The simulated data sets used in PIPI2 generated by <strong>AlphaPeptDeep</strong>.</p><p>Zeng, Wen-Feng, Xie-Xuan Zhou, Sander Willems, Constantin Ammar, Maria Wahle, Isabell Bludau, Eugenia Voytik, Maximillian T. Strauss, and Matthias Mann. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics." <i>Nature Communications</i> 13, no. 1 (2022): 7238.</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.