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9,113 results for “Sampling”
Sample MD trajectory
<p>MD trajectory used for training graph convolutional neural networks as coarse grained force fields as described in the following publications:</p> <ul> <li>E. Ricci, G. Giannakopoulos, V. Karkaletsis, D. N. Theodorou, N. Vergadou. 2022. "<em>Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls</em>". In Proceedings of 12th Conference on Artificial Intelligence (SETN). ACM, New York, NY, USA, 7 pages. <a href="https://doi.org/10.1145/3549737.3549793">https://doi.org/10.1145/3549737.3549793</a>. <strong>Open access</strong> <a href="../record/7078577">https://zenodo.org/record/7078577</a></li> <li>Gerakinis, D.-P., Ricci, E., Giannakopoulos, G., Karkaletsis, V., Theodorou, D. N., & Vergadou, N. (2024). <em>Machine Learning-Based Coarse Grained Interaction Potentials for Molecular Systems</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.10501037">https://doi.org/10.5281/zenodo.10501037</a></li> </ul> <p>The compressed archive contains input and output files for a NVT molecular dynamics simulation of a system containing 500 molecules of liquid benzene at 300 K, performed using LAMMPS.</p> <p>The reference code used to train the model, with detailed usage instructions, is available at: <a href="https://github.com/ml-multimem/schnetpack-for-bulk-systems">https://github.com/ml-multimem/schnetpack-for-bulk-systems</a></p>
Fig. 3 in Broad Taxon Sampling of Ciliates Using Mitochondrial Small Subunit Ribosomal DNA
Fig. 3. Concatenated mitochondrial and nuclear SSU-rDNA tree inferred from an alignment of 2333 included characters. Most likely ML tree is shown; the BI tree was the same for well-supported nodes. Node support is as in Fig 1.
Fig. 2 in Broad Taxon Sampling of Ciliates Using Mitochondrial Small Subunit Ribosomal DNA
Fig. 2. Nuclear SSU-rDNA tree inferred from an alignment of 1543 included characters. The most likely ML tree is shown; the BI tree was the same for well-supported nodes. Node support is as in Fig 1.
Fig. 1 in Broad Taxon Sampling of Ciliates Using Mitochondrial Small Subunit Ribosomal DNA
Fig. 1. Mitochondrial SSU-rDNA tree inferred from an alignment of 790 included characters. The most likely ML tree is shown; the BI tree was the same for well-supported nodes. Node support is shown as: ML bootstraps/BI posterior probability. Values ≤ 50 are shown as "-".
IW-NET sample data-set: River Weser IW network
<p>This data-set provides a sample of the data that was utilized for the analysis performed in the context of the IW-NET research project. The data have been collected via publicly available sources and are offered in this package as a sample. The use case the data refer to is River Weser, in northern Germany. The following table explains the contents of each of the uploaded files.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserWaterway.json/content" target="_blank" rel="noopener noreferrer">RiverWeserWaterway.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserRelationsWaysNodes.json/content" target="_blank" rel="noopener noreferrer">RiverWeserRelationsWaysNodes.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/IWTWeather.json/content" target="_blank" rel="noopener noreferrer">IWTWeather.json</a></div> </td> <td>Weather reports from 6 stations in the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/unCitiesDE.json/content" target="_blank" rel="noopener noreferrer">unCitiesDE.json</a></td> <td>UN/LOCODE data in json format.</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/MMSI.xlsx/content" target="_blank" rel="noopener">MMSI.xlsx</a></div> </td> <td>Correspondance of MMSI codes to Vessel registration country</td> </tr> </tbody> </table> <p>These resources were combined with AIS data logs from vessels active in the area - which, for legal reasons, cannot be made publicly available to provide powerful insights into the logistics operations and their intricacies.</p>
Dataset: Maurel et al. "Hayabusa 2 returned samples reveal a weak to null magnetic field during aqueous alteration of Ryugu's parent body"
<p>Samples: C0005 and A0154a from asteroid Ryugu (JAXA Hayabusa 2 mission), CI chondrite Orgueil, CM2 chondrite Daoura 003</p> <ul> <li>C0005: NRM, ARM, IRM demagnetization and anisotropy of ARM (AARM), IRM acquisition</li> <li>A0154a: NRM demagnetization, AARM</li> <li>Orgueil: NRM and ARM demagnetization</li> <li>Daoura 003: NRM demagnetization</li> </ul>
Tree crowns of the north-west corner of the permanent sample area in the Kaluzhskiye Zaseki Nature Reserve
<p>The studies were conducted in the Kaluga Zaseki Nature Reserve on a permanent sample plot (PSP) established in an old-growth broadleaved forest. The stand on the PSP has a complex structure, consisting of several tiers. There are 6 species of broad-leaved trees in the stand: oak (Quercus robur), ash (Fraxinus excelsior), elm (Ulmus glabra), sharp-leaved maple (Acer platanoides), field maple (A. campestre), linden (Tilia cordata) and 2 small-leaved trees - birch (Betula spp.) and aspen (Populus tremula). The oldest oak trees are about 300 years old. The size of the PPP is 440 × 200 m, this work was done on a 40 × 40 m plot located in the northwest corner of the PSP. For tree detection, orthophotomaps were used based on aerial photography materials taken with a DJI Phantom IV Pro quadcopter. Photogrammetric processing was carried out in Agisoft Metashape software (version 1.6.1.10009).</p> <p>The data set contains two fragments of multi-season orthophotos in tif format and corresponding files in shp., dbh. and shx. formats, which contain information on crown boundaries and species of marked trees.</p>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
Data From: what mandrills leave behind: using fecal samples to characterize the major histocompatibility complex in a threatened primate
<p>The major histocompatibility complex (MHC) can be useful in guiding conservation planning because of its influence on immunity, fitness, and reproductive ecology in vertebrates. The mandrill (<em>Mandrillus sphinx</em>) is a threatened primate endemic to central Africa. Considerable research in this species has shown that the MHC is important for disease resistance, mate choice, and reproductive success. However, all previous MHC research in mandrills has focused on an inbred semi-captive population, so their genetic diversity may have been underestimated. Here we expand our current knowledge of mandrill MHC variation by performing next-generation sequencing of non-invasively collected fecal samples from a large wild horde in central Gabon. We observe MHC lineages and alleles shared with other primates, and we uncover 45 putative new class II MHC DRB alleles, including representatives of the DRB9 pseudogene, which has not previously been identified in mandrills. We also document methodological challenges associated with fecal samples in NGS-based MHC research. Even with high read depth, the replicability of alleles from fecal samples was lower than that of tissue samples, and allele assignments are inconsistent between sample types. Further, the common assumption that variants with very high read depth should represent true alleles does not appear to be reliable for fecal samples. Nevertheless, the use of degraded DNA in the present study still enabled significant progress in quantifying immunogenetic diversity and its evolution in wild primates.</p>
MalwareBench: Malware samples are not enough
<p>The prevalent use of third-party components in modern software development, coupled with rapid modernization and digitization, has significantly amplified the risk of software supply chain security attacks. Popular large registries like npm and PyPI are highly targeted malware distribution channels for attackers due to their heavy growth and dependence on third-party components. Industry and academia are working towards building tools to detect malware in the software supply chain. However, a lack of benchmark datasets containing both malware and neutral packages hampers the evaluation of the performance of these malware detection tools. The goal of our study is to aid researchers and tool developers in evaluating and improving malware detection tools by contributing a benchmark dataset built by systematically collecting malicious and neutral packages from the npm and PyPI ecosystems. </p> <p>MalwareBench is a labeled dataset aimed at aiding researchers and tool developers in evaluating and improving malware detection tools. It comprises 20,792 packages (of which 6,659 are malicious) collected systematically from the npm and PyPI ecosystems. The dataset is constructed by amalgamating pre-existing malware datasets with Socket's internal benchmark data and incorporating both popular and newly released packages.</p> <p> </p> <h3>Description of the data and file structure</h3> <p>MalwareBench included malicious and neutral packages containing package names, versions, release types, and the ground truth of the package. We annotated the ground truth label of packages in two groups:</p> <ol> <li><strong>Malware</strong>: Packages that are written intentionally to carry out harmful actions and intended to perform an unauthorized process that will have an adverse impact on the confidentiality, integrity, or availability of a system.</li> <li><strong>Neutral</strong>: Packages with no discovered malware.</li> </ol> <p>In addition, we included additional metadata for packages, including the file path, file size, total number of files, file size, package size, file extension, and package group.</p> <h3>Sharing/Access information</h3> <p>We added a sample of our CSV file here. However, a complete dataset is hosted on <a href="https://github.com/MalwareBench">GitHub</a>. Since our dataset contains malware from the real world, some packages may contain sensitive information. We will distribute the dataset upon reasonable request based on ethical considerations of the purpose for using the data. After evaluating the reason for using the dataset, we will provide GitHub access. Please send an email to the authors to access the dataset. </p> <p>The data was derived from the following sources:</p> <ul> <li><a href="https://link.springer.com/chapter/10.1007/978-3-030-52683-2_2">Backstabber’s knife collection: A review of open source software supply chain attacks.</a></li> <li><a href="https://arxiv.org/pdf/2002.01139.pdf">Towards measuring supply chain attacks on package managers for interpreted languages</a>.</li> <li><a href="https://arxiv.org/abs/2309.11021">An Empirical Study of Malicious Code In PyPI Ecosystem.</a></li> <li><a href="https://github.com/datadog/malicious-software-packages-dataset">Open-Source Dataset of Malicious Software Packages</a></li> <li><a href="https://socket.dev/">Socket Internal benchmark Dataset </a></li> </ul>
Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples'. </p> <p>The following files are included:</p> <ul> <li>Plots: Additional plots not in the paper;</li> <li>Code: Python scripts to generate trajectories and perform classification/regression;</li> <li>best_algo.csv : Labels for the classification;</li> <li>performances.csv : Performances used for the regression;</li> <li>SA_parameters.csv : SA parameters for all machine learning tasks;</li> <li>irace_scenario.txt : scenario used for the tuning.</li> </ul>
Figure 1. Map showing the sampling site. A, B in A new species of Leipsuropus Stebbing, 1899 (Amphipoda: Podoceridae) from Japan
Figure 1. Map showing the sampling site. A, B, map showing the sampling site in Japan and the Kumano Sea. Maps were generated by using GMT6 (A, Wessel et al., 2019) or based on GSI Maps (B, https://maps.gsi.go.jp).
Discrete Flow Cytometry of Underway Samples from TN427 Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the University of Washington School of Oceanography 2024 undergraduate senior thesis research cruise (TN427) from American Samoa. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: [https://github.com/fribalet/FCSplankton](https://github.com/fribalet/FCSplankton)</p>
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling).</p>
Human Colorectal Tissue OCT Dataset: Neoplastic and Non-Neoplastic Samples from Chorioallantoic Membrane (CAM) Assays
<p>A commercial OCT system (Telesto II-1325 LR spectral domain OCT (SD-OCT)) was used to imaging two colon cell lines implanted in a chick embryo chorioallantoic membrane (CAM) assay. This is a high-performance imaging system designed for in vivo and ex vivo imaging of biological tissues.</p> <p>The CAM is a highly vascularized extra-embryonic membrane connected to the developing embryo through an easily accessible circulatory system that allows the successful engraftment of a variety of foreign tissues, such as tumor explants or cancer cell lines. Neoplastic and non-neoplastic tumors were developed from RKO and NCM460 cell lines, respectively.</p> <p> </p> <p>OCT B-scan images were collected from 30 CAM models, 15 with RKO-cells and 15 with NCM460-cells. The collected images are avalilable in format <strong>OCT </strong>format (<strong>Neoplastic_OCT.zip/Non-neoplastic_OCT.zip</strong>), <strong>TIFF </strong>format (<strong>Neoplastic_tiff.zip/Non-neoplastic_tiff.zip</strong>), and <strong>MAT </strong>format (<strong>Neoplastic_mat.zip/Non-neoplastic_mat.zip</strong>).</p> <p> </p> <p>Since, the attenuation of near-infrared light in biological samples has proven to be a powerful tool for tissue characterization the <strong>attenuation coefficient of light</strong> was calculated for each image. This data is available in the <strong>Neoplastic_processed.zip</strong> and <strong>Non-neoplastic_processed.zip</strong> folders.</p>
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site.
Fig. 1 in Evidence for intercontinental parasite exchange through molecular detection and characterization of haematozoa in northern pintails (Anas acuta) sampled throughout the North Pacific Basin
Fig. 1. Approximate locations in North America and East Asia at which northern pintail tissue samples were collected during 2011–2012 to test for haemosporidian infection. Regions (i.e. Alaska, California, and Japan) and sub-regions (Koyukuk-Nowitna NWR, Yukon-Kuskokwim Delta NWR, Izembek NWR, Sacramento Valley, San Joaquin Valley) for sampling locations are indicated (NWR = National Wildlife Refuge). The number of tissue samples per location is indicated in parentheses. Sample tissue was whole blood unless indicated by an asterisk (signifying wing muscle tissue).
Figure 3 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 3. Principal component analysis (PCA) for Xiphopenaeus spp. abundance and environmental variables in Anchieta region. The samples were collected between February/2013 and February/2015. Ab: Abundance; Gr: Granulometry; O.M: Organic Matter; Sal: Salinity: Temp: Temperature.
Figure 2 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 2. Boxplot of Xiphopenaeus spp. abundance at each collection point = transects, (A) and season (B) between February/2013 and February/2015. p1: Point 1; p2: Point 2; p3: Point 3. *Statistically significant difference.
Figure 6 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 6. Percentage values of gonadal development stages of Xiphopenaeus spp. at sampling points = transects, (A and B) and sampling period (C and D). Males (A and C) and females (B and D). Immature (IM), rudimentary (RU), developing (ED) and developed (DE) at each sampling point from February/2013 to February/2015. P1: Point 1, P2: Point 2, P3: Point 3.
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.