Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
20
datasets available to search
ShareScore release 0.9.0
Dataset results
20 results for “source determination”
Stable isotopes of Mya arenaria during 2010 and 2011 to determine food sources
Several sites in Plum Island Sound, the mainstem of the Parker River and Rowley River were selected and sampled during 2010 and 2011 for 2 and 4 inch intertidal clams on a monthly basis. The tissue of these clams were analyzed for stable isotopes to determine the food source of these clams. Water samples were also filtered and plants were collected to determine their isotopic composition as well.
Testing the Fidelity of Paleopole Determinations from Multidirectionally Magnetized Lunar Crustal Anomaly Source Bodies
<p>Code and data for "Testing the Fidelity of Paleopole Determinations from Multidirectionally Magnetized Lunar Crustal Anomaly Source Bodies ". See readme.txt</p>
Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use pre-downloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>(5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
<div> <p>SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data.</p> </div>
Crater Catalogue for Age Determination of the Chang'e-6 Mare Unit and Key Ejecta Source Craters
<p><strong>This page archives data used in the following article:<br></strong></p> <p><strong>Xu, L., Qiao, L., Xie., M., Wang, Y., Zhu, M-H., & Yan, J. (2024). Chronology, Local Stratigraphy, and Foreign Ejecta Materials at the Chang'e-6 Landing Site: Constraints on the Provenance of Samples Returned from the Moon's Farside. <em>Geophysical Research Letters</em>, 51. DOI: <span>10.1029/2024GL111311</span></strong></p> <p> </p> <p><strong>They include the Crater Catalogue for Age Determination of the Chang'e-6 Mare Unit and Key Ejecta Source Craters:</strong></p> <p><strong>CE6_MareUnit.zip: ArcGIS shapefile for the CRATER and AREA of the mare unit;</strong></p> <p><strong>CE6_5kmCircle_NAC.zip: ArcGIS shapefile for the CRATER and AREA of a 5 km-sized circle across the Chang'e-6 landing site;</strong></p> <p><strong>ChaffeeS.zip, White.zip, Vavilov.zip, and White'.zip: ArcGIS shapefile for the CRATER and AREA of key ejecta source craters Chaffee S, White, Vavilov, and White', respectively.</strong></p>
Linked collectors and determiners for: A mountain of millipedes VIII. The genus Aquattuor Frederiksen, 2013 revisited - a new species from the Udzungwa Mts, Tanzania, another from the Nguru Mts, and introduction of the first pair of male legs as a source of taxonomic characters (Diplopoda, Spirostreptida, Odontopygidae).
Natural history specimen data linked to collectors and determiners held within, "A mountain of millipedes VIII. The genus Aquattuor Frederiksen, 2013 revisited - a new species from the Udzungwa Mts, Tanzania, another from the Nguru Mts, and introduction of the first pair of male legs as a source of taxonomic characters (Diplopoda, Spirostreptida, Odontopygidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c906d9cd-cfcb-43db-a24c-b57b39d9184b">https://bionomia.net/dataset/c906d9cd-cfcb-43db-a24c-b57b39d9184b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c906d9cd-cfcb-43db-a24c-b57b39d9184b">https://gbif.org/dataset/c906d9cd-cfcb-43db-a24c-b57b39d9184b</a>. Formatted as a Frictionless Data package.
Table (1) and (2): Determining Maximum Power Density for Locations One to Nine and Associating Frequencies to Sources
<p>Table 1 details the maximum NIR level in dBm, the corresponding phase, the area for that location which are all used to determine the maximum power density.</p> <p>Table 2 details the frequency range used for this project and denotes all the sources available within each frequency band along with the accuracy of the sources. The accuracy includes being a confirmed source or a tested source.</p>
Boron elemental and isotopic determination via the BF diatomic molecule using high-resolution continuum source graphite furnace molecular absorption spectrometry
<p>This dataset contains the raw data corresponding to the figures of the publication DOI: 10.1039/d3ja00420a</p>
Radio-Source Tracker: Autonomous Attitude Determination on a Radio Interferometric Swarm: supplementary dataset
<p>This repository contains the data files used to generate the figures presented in the following article:<br> Rouillé, E. et al. (2023) "Radio-Source Tracker: Autonomous Attitude Determination on a Radio Interferometric Swarm", submitted to Radio Science.<br> (Pre-print Arxiv, TBD)</p> <p>The data are the output of various runs of the simulation pipeline.<br> Simulations were run for various interferometer configurations, frequencies, noise levels and random draws.<br> This pipeline and its related package can be found online at the following address:<br> https://gitlab.obspm.fr/erouille/noire_simulation/</p>
Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code
<p>A replication package for the paper "Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code", Journal of Empirical Software Engineering.</p>
Data for PASP paper: Optimal photometry of point sources: Joint source flux and background determination on array detectors - from theory to practical implementation
<p>High-resolution figures for paper "Optimal photometry of point sources: Joint source flux and background determination on array detectors - from theory to practical implementation", accepted for publication in The Publications of the Astronomical Society of the Pacific (PASP).</p>
WildfireDB: An Open-Source Dataset Connecting Wildfire Spread with Relevant Determinants
<p>Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography. Our dataset, named <em>WildfireDB</em>, contains over 17 million data points that capture how fires spread in the continental USA in the last decade. The paper accompanying this dataset is part of the 2021 Neural Information Processing Systems (NeurIPS) Dataset and Benchmark Track. The paper describes the algorithmic approach used to create and integrate the data, describe the dataset, and present benchmark results regarding data-driven models that can be learned to forecast the spread of wildfires.</p> <p> </p> <p>Please see https://colab.research.google.com/drive/1cm2Z4E0HzXMAcuUrE26wHXL2FS_pIj3t?usp=sharing for an introduction about how to load the database using python (pandas).</p>
Datasets: Carbon sources and pathways for citrate secreted by human prostate cancer cells determined by NMR tracing and metabolic modeling
<p>Zipped NMR datasets:</p> <p>data1 LNCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate</p> <p>data2 LNCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data3 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data4 VCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data5 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data6 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data7 VCaP medium with [2-<sup>13</sup>C]pyruvate -glucose +aspartate</p> <p>data8 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data9 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data10 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data11 LNCaP and VCaP medium with and without zinc(II)</p> <p>data12 LNCaP and VCaP medium with and without zinc(II)</p> <p>data13 LNCaP and VCaP medium with and without zinc(II)</p> <p>data14 LNCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data15 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data16 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data17 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data18 VCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data19 VCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data20 VCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data21 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data22 LNCaP medium with [2-<sup>13</sup>C]pyruvate +citr. Spiking</p> <p>data23 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data24 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data25 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data26 VCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data27 VCaP medium with [2-<sup>13</sup>C]pyruvate -glucose +aspartate</p> <p>data28 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data29 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data30 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data31 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data32 VCaP medium with [2-<sup>13</sup>C]pyruvate -glucose +aspartate</p> <p>data33 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data34 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data35 LNCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate</p> <p>data36 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data37 LNCaP medium with [5-<sup>13</sup>C]glutamine + citr. spiking</p>
Data from: Independent sources of condition dependency and multiple pathways determine a composite trait: lessons from carotenoid-based plumage colouration
Many color ornaments are composite traits consisting of at least four components, which themselves may be more complex, determined by independent evolutionary pathways, and potentially being under different environmental control. To date, little evidence exists that several different components of color elaboration are condition-dependent and no direct evidence exists that different ornamental components are affected by different sources of variation. For example, in carotenoid-based plumage coloration, one of the best-known condition-dependent ornaments, color elaboration stems from both condition-dependent pigment concentration and structural components. Some environmental flexibility of these components has been suggested, but specifically which and how they are affected remains unknown. Here we tested whether multiple color components may be condition-dependent, by using a comprehensive 3 x 2 experimental design, in which we carotenoid supplemented and immune challenged great tit nestlings (Parus major) and quantified effects on different components of coloration. Plumage coloration was affected by an interaction between carotenoid availability and immune challenge. Path analyses showed that carotenoid supplementation increased plumage saturation via feather carotenoid concentration and via mechanisms unrelated to carotenoid-deposition, while immune challenge affected feather length, but not carotenoid concentration. Thus, independent condition-dependent pathways, affected by different sources of variation, determine color elaboration. This provides opportunities for the evolution of multiple signals within components of ornamental traits. This finding indicates that the selective forces shaping the evolution of different components of a composite trait and the trait's signal content may be more complex than believed so far, and that holistic approaches are required for drawing comprehensive evolutionary conclusions.
Data from: Independent sources of condition dependency and multiple pathways determine a composite trait: lessons from carotenoid-based plumage colouration
Open the record for dataset details and reuse information.
Sources of oceanic freshwater content along the western Antarctic Peninsula (PAL-LTER Study Region) determined by the stable isotope composition (d18O) of seawater.
The oceanic distribution of d18O is determined largely by the same processes that control salinity. Surface d18O reflects the magnitude and spatial distribution of freshwater inputs, and it is a conservative tracer in the ocean interior. The great benefit of d18O is obtained from the circumstances under which it exhibits behavior different to that of salinity. One such circumstance derives from the salinity and d18O values in precipitation, with salinity being constant with latitude (typically zero), while in general d18O in precipitation becomes progressively isotopically lighter toward the poles. This results in glacial ice (which derives from high-latitude precipitation) being very isotopically light, enabling d18O to be a useful tracer of glacial discharge to the ocean (e.g., Schlosser et al. 1990; Weiss et al. 1979). Another difference occurs in regions influenced by sea ice, which greatly affects salinity during its formation/melt cycle but has only minimal impact on d18O. This decoupling of the two tracers allows them to be used in tandem to quantitatively separate freshwater inputs from sea ice melt and those from meteoric sources (precipitation plus glacial discharge). For this, a simple three-endmember mass balance can be used. For details please see Meredith, M. P., H. J. Venables, A. Clarke, H. W. Ducklow, M. Erickson, M. J. Leng, J. T. M. Lenaerts, and M. R. van den Broeke. 2013. The freshwater system west of the Antarctic Peninsula: Spatial and temporal changes. Journal of Climate 26:1669-1684.
Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources
GEO Series GSE116246. Saccharomyces cerevisiae. 60 samples. Type: Expression profiling by high throughput sequencing.
Natural History Study to Determine Drug Metabolism Phenotype and Appropriate Germline Source DNA in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplant
ClinicalTrials.gov study NCT06856226. IPD Sharing: YES. Countries: 1. Publications: 0.
Modulation of Polar Auxin Transport Identifies the Molecular Determinants of Source-Sink Carbon Relationships and Sink Strength in Poplar
GEO Series GSE232245. Populus tremula x Populus alba. 64 samples. Type: Expression profiling by high throughput sequencing.
Table (1) and (2): Determining Maximum Power Density for Locations One to Nine and Associating Frequencies to Sources
<p>Table 1 details the maximum NIR level in dBm, the corresponding phase, the area for that location which are all used to determine the maximum power density.</p> <p>Table 2 details the frequency range used for this project and denotes all the sources available within each frequency band along with the accuracy of the sources. The accuracy includes being a confirmed source or a tested source.</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.