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109,972 results for “descriptions”
North Carolina Outer Banks, USA Coastal Foredune Sediment Cores - Grain Size Data & Core Log Descriptions
<p>This repository includes sediment core data collected at seven sites along the northern Outer Banks, North Carolina, USA. From north to south, the sites include Pine Island, Corolla Reserve, Duck, the US Army Corps of Engineers Field Research Facility (FRF) North, FRF South, Southern Shores (i.e., Hillcrest Beach Access), and Nags Head (Bonnett St. Beach Access).</p><p>At each site, internal dune sedimentology and stratigraphy were characterized using sediment vibracores, each 1.5–2.2 m long, collected along a cross-shore transect from the dune toe to the dune heel. Coring locations were selected based on dune morphology to capture the stratigraphy of the dune toe, stoss slope, primary dune crest, lee slope, swale, and secondary dune crest, as applicable. Sediment core locations were documented using RTK-GPS and are included in the .kmz file.</p><p>All sediment cores were split, photographed, described for sedimentary structures, texture (as compared to standards), mineralogy, and color (Munsell, 2012). Sediment cores were described using the Modified Burmister System in 10-cm intervals, with additional intervals added as needed to capture stratigraphic units with thicknesses less than 10 cm but greater than 1 cm. Sediment core log descriptions are included in the NOAA_NCDunes_Vibracore_CoreLogs.xlsx data file.</p><p>Sediment size and shape were analyzed from oven-dried samples using a CAMSIZERX2Ⓡ. These data are included in the Dune_Grain_Size_camsizer_outputs.csv data file. Metrics reported for each sample include the following: Site, Core ID, Sample Number, Depth (cm below ground surface), Elevation (m, NAVD88), D2 (mm), D5 (mm), D10 (mm), D16 (mm), D25 (mm), D50 (mm), D75 (mm), D84 (mm), D90 (mm), D95 (mm), D98 (mm), average grain symmetry, average grain sphericity, average grain aspect ratio, percent pebble, percent granule, percent very coarse sand, percent coarse sand, percent medium sand, percent fine sand, percent very fine sand, and percent silt.</p><p><strong>More details regarding these measurements can be found in the following manuscript:</strong></p><p>Davis, E.H., Hein, C.J., Cohn, N., White, A.E., Zinnert, J.C. Differences in internal sedimentologic and biotic structure between natural, managed, and constructed coastal foredunes (in review).</p>
Uncertainty in Migration Scenarios. QuantMig Project Deliverable D9.2 Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Barker and Bijak (2021), Uncertainty in Migration Scenarios, QuantMig Project Deliverable D9.2. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7709443).</p>
Erosion Rates, Soil Core Descriptions and Organic Matter on the Virginia Coast
These data include stratigraphic, organic matter, and organic carbon analyses of sediment cores, as well as values used to calculate the time-averaged carbon erosion rate for the central 10 islands of the Virginia Barrier Island chain.
Fedora and Debian software package dependency networks along with description text associated with nodes
<p>Fedora (version 28) and Debian (version 9.5) software package dependency networks along with description text associated with nodes. Also includes learned vectors by using PCTADW-* as in "Kexuan Sun, Shudan Zhong, and Hong Xu. 2020. Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks. 2020 BigGraphs Workshop at IEEE BigData 2020."</p>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
<p>The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density-dependent relativistic mean-field description of nuclear matter <a href="https://arxiv.org/abs/2201.12552">Malik et al 2022</a>. The EOS is subjected to a minimal number of constraints based on nuclear saturation properties and the low-density pure neutron matter EOS obtained from a precise next-to-next-to-next-to-leading order (N$^{3}$LO) calculation in chiral effective field theory ($\chi$EFT). The number of final sample parameters corresponding to the posterior sets is around fourteen thousand. We present five EOSs among them, namely DDBl, DDBm, DDBu1, DDBu2, and DDBx. The DDBl, DDBm, DDBu2 were chosen so that the radius of the 1.4$M_\odot$ star has the lower limit, a medium value, and the upper limit of the 90% CI for the conditional probabilities $P(R|M)$. We have also included DDBu1 that has a slightly lower $R_{1.4}$ than the upper limit but lies completely inside the 90% CI for the conditional probabilities $P(R|M)$. The DDBx is the one that predicts a maximum mass of 2.5$M_\odot$ and has the following nuclear matter properties, $K_0=300$ MeV, $J_{sym,0}=30$ MeV and $L_{sym,0}=39$ MeV.</p> <p>We also release our entire sets of ~14K NS matter EOS. All the EOSs are for NS core and starting baryon density is 0.04 fm$^{-3}$. One needs to add their own choice of crust EOS for the star properties calculation. The uncertainty in star properties for the choice of the different crust has been discussed in Section 2.1 of the manuscript (arxiv: 2201.12552). </p> <pre> To extract the entire sets of ~14K NS matter EOS files, one needs to follow the steps, 1) unzip DDB_EOS_14K.zip ----------------------------Note------------------------------------- All the eos files have three columns baryon density (fm-3), energy density (MeV.fm-3), and pressure (MeV.fm-3). The starting density is 0.04 fm-3, as it is NS core eos. One needs to add their own choice of crust eos in order to calculate NS properties. ---------------------------------------------------------------</pre> <p> </p> <p> </p>
Dataset of "Atomic-level description of thermal fluctuations in inorganic lead halide perovskites" publication
<p>The "Zenodo_22-02-2022.zip" file contains a "files" folder and a jupyter notebook to plot the figures reported in the publication. The "files" folder contains several subfolders with the txt files required to plot the figures.</p> <p>The "XAS_simulations.zip" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.</p>
Descriptions of SNSF-funded research projects
<p>This repository contains the data to replicate the analyses performed in Meier, D. S., Mata, R., & Wulff, D. U. (2021). text2sdg: An R package to Monitor Sustainable Development Goals from Text. <em>arXiv preprint arXiv:2110.05856</em>.</p> <p>The <a href="../api/records/11060662/draft/files/GrantWithAbstracts.csv/content" target="_blank" rel="noopener noreferrer">GrantWithAbstracts.csv</a> data is originally provided by the Swiss National Science Foundation and can also be downloaded from their <a href="https://data.snf.ch/datasets">website</a>. This data contains information on research projects funded by the Swiss National Science Foundation between 1975 and 2022. Among other things, the data provides information on what the funded projects were about and how much funding was provided.</p> <p>The <a href="../api/records/11060662/draft/files/backtrans_table.RDS/content" target="_blank" rel="noopener noreferrer">backtrans_table.RDS</a> data contains 1,500 randomly selected projects from the <a href="../api/records/11060662/draft/files/GrantWithAbstracts.csv/content" target="_blank" rel="noopener noreferrer">GrantWithAbstracts.csv</a> data that were translated from English to German and then from German back to English.</p> <p>The <a href="../api/records/11060662/draft/files/benchmark_table_revision.rds/content" target="_blank" rel="noopener noreferrer">benchmark_table_revision.rds</a> data contains runtimes from benchmarking the text2sdg R package. </p>
Excel template for the aggregate database on descriptive representation of the ActEU project
<p>This is the Excel template used to structure the databases that provide data at the legislature / party level for each of the six countries studied in Tasks 4.1 and 4.2 of the ActEU project.</p>
Anypodetus leucothrix and Anypodetus londti species descriptions in SDD format
<p>Natural-language species description of Anypodetus leucothrix and Anypodetus londti in SDD format exported from Lucid Builder 4.0.</p>
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Project description, plot characteristics and design
Although temperate forests are generally thought of as N-limited, resource optimization theory predicts that ecosystem productivity should be co-limited by multiple nutrients. These ideas are represented in the Multi-Element Limitation (MEL) model (Rastetter et al. 2012). To test the patterns of resource limitation predicted by MEL, we are conducting nutrient manipulations in three study sites in New Hampshire: Bartlett Experimental Forest (BEF), Hubbard Brook Experimental Forest (HBEF), and Jeffers Brook in the White Mountain National Forest. We are monitoring stem diameter, leaf area, sap flow, foliar chemistry, leaf litter production and chemistry, foliar nutrient resorption, root biomass and production, mycorrhizal associations, soil respiration, heterotrophic respiration, N and P availability, N mineralization, soil phosphatase activity, soil carbon and nitrogen, nutrient uptake capacity of roots, and mineral weathering. These data can be found in the EDI repository, using the search term "MELNHE" (http://portal.edirepository.org), and through the data catalog on https://hubbardbrook.org, using the same search term. This data package is referenced by the MELNHE datasets, and includes a datatable of site descriptions and a pdf file with the project description, and diagrams of plot configuration. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Watershed 3 Lateral Weathering Pedon Descriptions
Soil pits and horizons were described and sampled as part of the Lateral Weathering project within Watershed 3, Hubbard Brook Experimental Forest, Woodstock, NH, USA from 2018-2020. Soil pits were dug and described using NRCS methods. When possible, physical samples were archived in the Hubbard Brook Sample Archive. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Main Cropping System Experiment Field Logs and treatment descriptions at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2020)
Dataset Abstract This dataset includes information about the LTER main site treatments, agronomic practices carried out on the treatments and approved site use requests. Most long-term hypotheses associated with the KBS LTER site are being tested within the context of the main cropping systems study. This study was established on a 48 ha area on which a series of 7 different cropping systems were established in spring 1988, each replicated in one of 6 ha blocks. An eighth never-tilled successional treatment, is located 200 m off-site, replicated as four 0.06 ha plots. Cropping systems include the following treatments: T1. Conventional: standard chemical input corn/soybean/wheat rotation conventionally tilled (corn/soybean prior to 1992) T2. No-till: standard chemical input corn/soybean/wheat rotation no-tilled (corn/soybean prior to 1992) T3. Reduced input: low chemical input corn/soybean/wheat rotation conventionally tilled (ridge till prior to 1994) T4. Biologically based: zero chemical input corn/soybean wheat rotation conventionally tilled (ridge till prior to 1994) T5. Poplar: Populus clones on short-rotation (6-7 year) harvest cycle T6. Alfalfa: continuous alfalfa, replanted every 6-7 years (converted to switchgrass in 2018) T7. Early successional community: historically tilled soil T8. Mown grassland community: never-tilled soil. For specific crops in a given year see the Annual Crops Summary Table. In 1993 a series of forest sites were added to the main cropping system study to provide long-term reference points and to allow hypotheses related to substrate diversity to be tested. These include: TCF. Coniferous forest: three conifer plantations, 40-60 years old TDF. Decidious forest: three deciduous forest stands, two old-growth and one 40-60 years post-cutting TSF. Mid-successional forest: three old-field (mid-successional) sites 40+ years post-abandonment. All share a soil series with the main cropping system treatments, and are within 5 km of all other sit
Leopard Cave ochres description and geochemical data
<p>The datasets present the macroscopic description and ICP-OES and ICP-MS geochemical data of archaeological ochres recovered at Leopard Cave rock art site (Erongo, Namibia). </p>
A studyforrest extension, an annotation of spoken language in the German dubbed movie ``Forrest Gump'' and its audio-description (validation analysis)
<p>This component contains the data of the analysis that we ran as a validation of the annotation of speech spoken in the research cut (Hanke et al., 2016) of the movie "Forrest Gump" (Zemeckis, 1994) and its audio-description. The corresponding paper is hosted on github (https://github.com/psychoinformatics-de/studyforrest-paper-speechannotation) and published in f1000research (https://doi.org/10.12688/f1000research.27621.1).</p>
Let the giants be named - Taxonomic description database for the isopod genus Bathynomus for the DELTA system
<p><span>Morphological characters have been extracted from recent relevant literature on <em>Bathynomus</em>, mainly from the six previously known species from the Atlantic Ocean and gulf of Mexico </span><span>(Magalhães and Young 2003; Lowry and Dempsey 2006; Shipley et al. 2016; Huang et al. 2022)</span><span>. To simultaneously maximize compactness and taxonomic value of the species description, mainly characters that have previously been interpreted as diagnostic at the species level have been included, omitting others which played no role in species delimitation. A taxonomic database has been newly set up using DELTA </span><span>(Dallwitz 1980, 1993; Dallwitz et al. 2006)</span><span>. Character states for all presently known Atlantic congeners have been scored to generate a natural language description and a new identification key </span><span>(Dallwitz 1974)</span><span> for the region as well as a species diagnosis. The species diagnosis has been composed based on the characters used in the identification key.</span></p> <p> </p>
162 Human Error Descriptions and Categorizations from a User Study
<p><i><strong>Software Engineers' Human Errors</strong></i></p><p>This dataset contains descriptions of 162 human errors experienced by software engineering students during a user study described in the following publication:</p><ul><li>Benjamin S. Meyers and Andrew Meneely. Taxonomy-Based Human Error Assessment for Senior Software Engineering Students. Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium. Forthcoming in 2024.</li></ul><p><i><strong>Included Files</strong></i></p><p>The "experienced_human_errors.csv" file contains a dataset of 162 human errors experienced during our user study. Participants documented their human errors in a Google Form with 8 questions.</p><p><i><strong>CSV Fields</strong></i></p><ul><li><strong>PARTICIPANT</strong>: Anonymous participant ID.</li><li><strong>INTERVIEW_DATE</strong>: Date of interview discussing human error.</li><li><strong>ID</strong>: Unique ID for experienced human error. Prefixed with "P1" for Phase 1 or "P2" for Phase 2.</li><li><strong>FINAL_CATEGORIZATION</strong>: Agreed upon T.H.E.S.E. categorization following discussion with interview facilitator.</li><li><strong>QUESTION_1</strong>: Anonymized participant answer to Question 1.</li><li><strong>QUESTION_2</strong>: Anonymized participant answer to Question 2.</li><li><strong>QUESTION_3</strong>: Anonymized participant answer to Question 3.</li><li><strong>QUESTION_4</strong>: Anonymized participant answer to Question 4.</li><li><strong>QUESTION_5</strong>: Anonymized participant answer to Question 5.</li><li><strong>QUESTION_6</strong>: Anonymized participant answer to Question 6.</li><li><strong>QUESTION_7</strong>: Anonymized participant answer to Question 7.</li><li><strong>QUESTION_8</strong>: Anonymized participant answer to Question 8.</li></ul><p><i><strong>Interview Questions</strong></i></p><ol><li>Please briefly describe the human error that you experienced.</li><li>If the human error you experienced resulted in a defect that was committed, please provide a link (or Git commit hash) to the commit below.</li><li>Is your human error a slip, lapse, or mistake?</li><li>Now, please examine the Taxonomy of Human Errors in Software Engineering (T.H.E.S.E.) and choose the specific human error that most accurately describes the human error you experienced. If you experienced multiple human errors, please submit this form once for each human error.</li><li>If there are other categories of human error that also describe the human error that you experienced, please note them here.</li><li>If you chose a 'General' or 'Other' category in Question (4), this question is required. Do you believe there is a missing human error category that better describes the human error that you experienced? If yes, please describe it below.</li><li>On a scale of 1 (not at all confident) to 5 (completely confident), how confident are you in your classification in the previous question?</li><li>Do you have any additional comments about this human error?</li></ol><p><i><strong>Anonymity</strong></i></p><p>Institutional Review Board approval for this research involving human subjects was granted by the Human Subjects Research Office at RIT on March 18, 2022. Participants signed an informed consent form acknowledging that (1) their participation was entirely voluntary and had no impact on their grades, and (2) their survey responses would be published in an anonymized format. All data released with this publication has been anonymized by replacing any personally identifiable information with participant identifiers.</p><p><i><strong>Contact</strong></i></p><p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p><p><i><strong>Acknowledgments</strong></i></p><p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
IODP Expedition 391 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
IODP Expedition 397T Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
IODP Expedition 383 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
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.