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7,849 results for “Conservation”
Regional Conservation Partnerships in New England 2009-2012
Across New England, a new model of regional collaboration is increasingly being used by land conservation trusts, watershed associations, state agencies and others. Regional conservation partnerships (RCPs) serve multiple purposes, such as coordinating among the various active groups in the region and allowing them to leverage funding and staff capacity. However, their essential missions are the same--protect more land from development. We use interviews, geographic information systems (GIS), and statistical analysis on 20 case studies to document RCP growth and characteristics and to analyze which attributes most contribute to their ability to conserve land. Along with well-known factors of organizational development, we find that the RCPs that match the size of the partnership region with the territory and capacity of the host partner organization are better able to achieve measurable conservation gains.
Community and Conservation Survey in Urban, Suburban and Rural Massachusetts 2013-2018
The dynamics of forest cover and the ecosystem services they provide are shaped by the land use and management decisions of thousands of individual landowners and the land use planning and conservation actions of towns and environmental organizations. Through an interdisciplinary investigation of the land use and forest conservation practices across two urban-to-rural transects between Boston and Central Massachusetts, we investigated the complex and coupled socio-ecological processes that shape the structure, function, and transformation of forested landscapes and how examine these processes may vary along urban-to-rural gradients. The survey data archived here is one element of this larger coupled natural-human systems project. The Community and Conservation Survey collected data regarding landowners’ attitudes and management practices on a variety of issues linked to conservation and the use of their own land. The objectives were to collect data that (a) increase our understanding of how landowners’ attitudes and behaviors vary across urban-to-rural gradients and (b) can be coupled with biogeochemical measurements across the study region to model variation in management behaviors.
Conservation Study in North Quabbin Region of Central Massachusetts 1900-1993
This study investigated the history of land protection in north central Massachusetts. For details on methods and results, please see the published paper (Golodetz, A.D. and D.R. Foster. 1997. History and importance of land use and protection in the North Quabbin region of Massachusetts (USA). Conservation Biology 11:227-235). The Abstract from the paper is reproduced below: "Evaluating the consequences and future of land protection requires broad temporal and spatial perspectives of ecological and cultural factors. We assessed the development of a system of protected areas comprising 37% of central Massachusetts in terms of changing rates and means of land protection. We compared protected areas to the surrounding matrix in terms of physical, biological, and historical features and used these results to raise issues concerning future planning. The rate, purpose, and means of land protection in the North Quabbin Region (168,312 ha) have been dynamic as a result of changes in cultural values and transformation of the landscape from predominantly agriculture to forest. Protected lands are managed by 25 federal and state agencies, private groups, and municipal departments and commissions and are physically and biologically typical of the regional landscape which results from (1) participation of diverse organizations with varied agendas; (2) predominance of large government acquisitions driven by landscape-scale criteria; and (3) absence of coordination among groups. The large area, relative homogeneity and largely undeveloped status of the North Quabbin Region suggest conservation goals distinct from those in the fragmented and extensively developed neighboring areas of the Connecticut River Valley and Cape Cod and Islands Region. Large tracts of forests, wetlands, and lakes in the North Quabbin Region provide (1) habitat for species requiring extensive, intact areas; (2) the opportunity to maintain broad-scale ecological processes; (3) connections to the regional con
Land Conservation and Human Demographics by Census Tract in New England 2014-2018
This dataset summarizes land protection, conservation prioritization layer scores, and human demographics within New England communities, defined as census tracts. This dataset was created to identify disparities in land protection according to metrics of social marginalization and assess how incorporating environmental justice criteria into land conservation prioritization systems might change conservation priorities.
Conserved regulation of RNA processing in somatic cell reprogramming
<p><strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.</p> <p><strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.</p> <p><strong>Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), are listed. Format, column and row names as in Data set 1.</p> <p><strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.</p> <p><strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.</p> <p><strong>Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. </strong>The file contains log fold changes, inferred by edgeR (<a href="http://bioconductor.org/packages/release/bioc/html/edgeR.html">http://bioconductor.org/packages/release/bioc/html/edgeR.html</a>), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for mouse genes.</p> <p><strong>Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for chimpanzee genes.</p> <p><strong>Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. </strong>The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7.</p> <p><strong>Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for mouse genes.</p> <p><strong>Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for chimpanzee genes.</p> <p><strong>Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. </strong>The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; <a href="http://www.ensembl.org/">http://www.ensembl.org/</a>). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. </strong>As in Data set 13, but for mouse alternative splicing events.</p> <p><strong>Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>As in Data set 15, but for mouse alternative splicing events.</p> <p><strong>Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15.</p> <p><strong>Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15.</p> <p><strong>Data set 19. Transcript expression across murine RNA-Seq time course data: estimated read counts. </strong>As in Data set 2, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 20. Transcript expression across murine RNA-Seq time course data: estimated transcript abundances. </strong>As in Data set 5, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 21. Quantification of alternative splicing events across murine RNA-Seq time course data. </strong>As in Data set 14, but for the time course data generated for the accompanying publication.</p>
Micrometeorological data from Etosha Heights Conservation Centre, Namibia, 2023-ongoing
This data set contains half-hourly micrometeorological data from six weather stations distributed across Etosha Heights Private Reserve in northern Namibia. Data collection began at the end of May 2023, and is ongoing. The data are part of a project funded by Colgate University's Picker Interdisciplinary Science Institute, in collaboration with Giraffe Conservation Foundation and the Namibia University of Science and Technology, aimed at better understanding animal movement. That data are being coupled with gps data from a variety of animals within the reserve and adjacent Etosha National Park.
Conservation and Economic Data for New England Towns 1990-2015
Land protection, whether public or private, is often controversial at the local level because residents worry about lost economic activity. We used panel data and a quasi-experimental impact-evaluation approach to determine how key economic indicators were related to the percentage of land protected. Specifically, we estimated the impacts of public and private land protection based on local area employment and housing permits data from 5 periods spanning 1990-2015 for all major towns and cities in New England. To generate rigorous impact estimates, we modeled economic outcomes as a function of the percentage of land protected in the prior period, conditional on town fixed effects, metro-region trends, and controls for period and neighboring protection. Contrary to narratives that conservation depresses economic growth, land protection was associated with a modest increase in the number of people employed and in the labor force and did not affect new housing permits, population, or median income. Public and private protection led to different patterns of positive employment impacts at distances close to and far from cities, indicating the importance of investing in both types of land protection to increase local opportunities. The greatest magnitude of employment impacts were due to protection in more rural areas, where opportunities for both visitation and amenity-related economic growth may be greatest. Overall, we provide novel evidence that land protection can be compatible with local economic growth and illustrate a method that can be broadly applied to assess the net economic impacts of protection.
A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)
<p>The peculiar microclimate inside cultural hypogeal sites needs to be carefully investigated. This study presents a methodology that aimed at providing a user-friendly assessment of the frequently occurring hazards in such sites. A Risk Index was specifically defined as the percentage of time for which the hygrothermal values lie in ranges that are considered to be hazardous for conservation. An environmental monitoring campaign that was conducted over the past ten years inside the Mithraeum of the Baths of Caracalla (Rome) allowed for us to study the deterioration before and after a maintenance intervention. The general microclimate assessment and the specific conservation risk assessment were both carried out. The former made it possible to investigate the influence of the outdoor weather conditions on the indoor climate and estimate condensation and evaporation responsible for salts crystallisation/dissolution and bio-colonisation. The latter took hygrothermal conditions that were close to wall surfaces to analyse the data distribution on diagrams with critical curves of deliquescence salts, mould germination, and growth. The intervention mitigated the risk of efflorescence thanks to reduced evaporation, while promoting the risk of bioproliferation due to increased condensation. The Risk Index provided a quantitative measure of the individual risks and their synergism towards a more comprehensive understanding of the microclimate-induced risks.</p>
On the Moreau–Jean scheme with the Frémond impact law: energy conservation and dissipation properties for elastodynamics with contact, impact and friction — data
<p>This deposit contains the data output of the systems described in <a href="https://hal.science/hal-04230941">On the Moreau–Jean scheme with the Frémond impact law. Energy conservation and dissipation properties for elastodynamics with contact impact and friction.</a> The codes that generated this data are available in another <a href="../records/10953181">deposit</a> archived on Zenodo, as well as in a GitHub repository archived on <a href="https://archive.softwareheritage.org/swh:1:dir:33ff6d960b70505c7939c0ce21c039cabbe1351c;origin=https://github.com/nickcollins-craft/On-the-Moreau-Jean-scheme-with-the-Fremond-impact-law;visit=swh:1:snp:72aede3d3a464732a36ef79c20ef07eebd1f9918;anchor=swh:1:rev:b63b68c25e72d23d7d9ee30225165fa0ebffb3c2">Software Heritage</a>, which is the preferred method of obtaining the codes. Two of the files in this deposit ("deformed_sliding_block_mesh.png" and "sliding_block_mesh.png") are required for one of the codes in the code deposit to run successfully ("block_mesh_plot.py", with the files assumed to be located in the folder specified in the data_folder variable of the file "path_file.py"), but the deposits are otherwise independent.</p>
Plant Atlas 2020 — British and Irish plant conservation statuses
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the conservation status tables presented on the Conservation tabs of species’ pages of the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>).</span></p>
Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation
<p>This dataset contains the raw and processed microscopy data that form the basis of our research article titled <em><a href="https://www.cell.com/heliyon/fulltext/S2405-8440(24)14817-7" target="_blank" rel="noopener">Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation</a></em> (DOI: <a href="https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fdoi.org%2F10.1016%2Fj.heliyon.2024.e38786/1/010201924b2cde3a-4e9b4379-d805-432d-b94e-11d8b32354dc-000000/-WTssLbV_1JADo7ZwuFgWEmy9i0=394" target="_blank" rel="noopener noreferrer">doi.org/10.1016/j.heliyon.2024.e38786</a>) publlished in the <a href="https://www.cell.com/">CellPress</a> journal <a href="https://www.cell.com/heliyon/home">Heliyon</a> (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/heliyon/vol/10/issue/19"><span><span>Volume 10, Issue 19</span></span></a>, 15 October 2024, e38786).<em> </em>The paper describes the mechanism underlying the binding of <a href="https://www.yeastgenome.org/locus/S000003141">yeast Xrn1</a> to the plasma membrane microdomain stabiliser <a href="https://doi.org/10.1016/j.cub.2017.11.073">eisosome</a> in a glucose-dependent manner. The images stored in the dataset were acquired with a <a href="https://www.iem.cas.cz/en/devices/zeiss-lsm-880-airyscan-en/">Zeiss LSM 880 confocal microscope</a> performed at the <a href="https://www.iem.cas.cz/en/department/microscopy-unit/">Microscopy Service Centre</a> of the <a href="https://www.iem.cas.cz/en/home-en/">Institute of Experimental Medicine CAS</a> supported by the MEYS CR (LM2023050 <a href="https://www.czech-bioimaging.cz/">Czech-Bioimaging</a>). Detailed step-by-step instructions for live microscopy sample preparation that we follow can be found at protocols.io: <a href="https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b">https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b</a>. The quantification of microscopy data was performed using our custom-developed Fiji and R scripts that can be found at <a href="https://github.com/jakubzahumensky/microscopy_analysis">https://github.com/jakubzahumensky/microscopy_analysis</a>. Their use is described in detail in the <a href="https://doi.org/10.1101/2024.03.28.587214">https://doi.org/10.1101/2024.03.28.587214</a>. For further information, please refer to the README file attached to the dataset.</p> <p>Note: This final version of datasets supplements the previous datasets of version 1 (DOI: <a href="https://doi.org/10.5281/zenodo.12748899">10.5281/zenodo.12748899</a>) and version 2 (DOI: <a href="https://doi.org/10.5281/zenodo.13772845">10.5281/zenodo.13772845</a>).</p>
Herbivore dung and parasite counts, Ol Pejeta Conservancy and Mpala Research Centre, Kenya (2015-2018)
Data package contains two datasets of dung surveys, one dataset of parasite egg measurements, and two camera trap datasets collected from Mpala Research Centre and Ol Pejeta Conservancy, Laikipia County, Kenya from November 2015-September 2018. Datasets are provided as part of the publication `Watering sources aggregate parasites with increasing effects in more arid conditions`. Source data files for figures in the manuscript are also provided here.
Data for: Global political responsibility for the conservation of albatrosses and large petrels
<p>Data derivatives from analysis of seabird tracking data. These data allow one to reproduce the results of the paper "Global political responsibility for the conservation of albatrosses and large petrels by Beal et al (in press). </p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles
<p><strong> Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles</strong></p> <p>- Authors: Pedro Jimenez, Luis Chacon, Mario Merino</p> <p>- Contact email: pejimene@ing.uc3m.es</p> <p>- Date: 2024-02-09</p> <p>- Keywords: electric propulsion, plasma simulation, magnetic nozzles, implicit particle-in-cell (PIC)</p> <p>- Version: 1.2</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.8081962</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0">Open Data Commons Attribution License</a></p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the journal article:</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0021999124000755?via%3Dihub">Pedro Jimenez, Luis Chacon, Mario Merino, "An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles"</a></p> <p>The data in this repository are the results of kinetic plasma simulations as described in the reference. For further information on the setup for the simulation please refer to the article.</p> <p><strong>Data Files</strong></p> <p>The data files are in .csv format. They were produced in Julia using <a href="http://csv.juliadata.org/stable/)">CSV.jl</a> and <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a> libraries.</p> <p>The files are organised following the order of the figures in the article. All the plots are 1D series, the first column corresponding to the x-axis data. Y-axis data is presented in the following columns, the total number of additional columns is equal to the number of line series. The title of each series is found in the first row of the .csv files. Please find below some specificalities in certain figures:</p> <p>- The columns for the time evolution in <strong>fig6_left.csv</strong> and<strong> fig6_right.csv </strong>(corresponding to the actual left and right columns in the figure i.e. cases A and B) contain a field tag followed by the corresponding time step (e.g. phi_500).</p> <p>- Due to the different number of nodes, steady state fields for cases A and B are saved in <strong>fig8_a-f.csv</strong> while cases AF and BF are saved in <strong>fig8_a-f_fine.csv</strong>.</p> <p>The rest of the data files should be self descripting</p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication asociated to the jounal article with DOI: <a href="https://doi.org/10.1016/j.jcp.2024.112826">10.1016/j.jcp.2024.112826</a></p> <p>The BibTex is also provided for the sake of convinience:</p> <pre>@article{jimenez2024implicit, title={An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles}, author={Jim{\'e}nez, Pedro and Chac{\'o}n, Luis and Merino, Mario}, journal={Journal of Computational Physics}, pages={112826}, year={2024}, publisher={Elsevier} }</pre> <p>Optionally the dataset can be cited by referencing the corresponding DOI:</p> <p><a href="https://doi.org/10.5281/zenodo.8081962">https://doi.org/10.5281/zenodo.8081962</a></p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Data for "Let's not wing it: Effective conservation of subterranean-roosting bats"
<p>Database as both excel (.xls) and tab-delimited (.csv) associated with the publication: </p> <p>Meierhofer M.B., et al. (2023) Let’s not wing it: Effective conservation of subterranean-roosting bats. <em>Conservation biology.</em></p> <p>Please refer to the main publication for a detailed description. An explanation of the database is available in the Metadata file uploaded alongside the database. R code to reproduce the analysis pipeline is available on GitHub:</p> <p>https://github.com/StefanoMammola/Analysis_Cave_bat_conservation.git</p>
3C dataverse: Community capitals, cover crops, & conservation agriculture in the U.S. corn-soybean belt, version 2.2
<p><strong>What? </strong></p> <p>A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.</p> <p><strong>Why? </strong></p> <p>Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.</p> <p><strong>How? </strong></p> <p>Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. </p> <p><strong>What is 'new' or corrected in version 2.2? </strong></p> <p><em>Edited/amended</em>: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. </p> <p><em>Added</em>: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. </p> <p><em>Omitted</em>: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell.</p> <p><strong>CRediT</strong>: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K</p> <p><strong>Acknowledgements</strong>: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance. </p>
An Explicit Primitive Conservative Solver for the Euler Equations with Arbitrary Equation of State: Dataset
<p>Contains the files for all simulations related to the publication:</p> <p>"An Explicit Primitive Conservative Solver for the Euler Equations with Arbitrary Equation of State: Dataset"</p> <p>paper: https://doi.org/10.1016/j.compfluid.2024.106340</p> <p>pre-print: https://doi.org/10.48550/arXiv.2404.07710</p>
Dataset from the Survey on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the responses from the participants in the survey: Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice.</p> <p>The main purpose of this survey was to explore how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C)<br>industry can adapt and better prepare to embrace the innovative principles and enabling technologies of Industry 5.0. This could ultimately result in<br>enhanced conservation practices for built cultural heritage.</p> <p> </p>
Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples
<p>The repository contains cross-sections of 83 wood samples derived from X-ray computed tomography (CT) data. The samples are a part of the LEIZA reference collection, which were created within the framework of the project "Mass Finds in Archaeological Collections", which was funded by the "Kulturstiftung des Bundes" and the "Kulturstiftung der Länder" from 15.04.2008 to 31.12.2011 as part of the "Program for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur).</p> <p>Around 10 years later, during the CuTAWAY project (ConservaTion And Wod AnalYses), the wood samples were digitized using an in-house laboratory X-ray CT system (Diondo d2, Germany) at HSLU with a nominal voxel size between 27 and 44 μm in order to analyse the structure of the interior. You can download the cross-sectional images of the data here. The 3D data acquisition was carried out during November 2019 - April 2021.</p> <p>The CuTAWAY project was funded by the German Research Association (DFG) and the Swiss National Science Foundation (SNSF) from 2019 to 2023 (CuTAWAY - Conservation and Wood Analyses, DFG - 416877131 and SNSF - 200021E_183684).</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.