Find research datasets worth reusing

Figure 9. Omobranchus zebra, a in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 9. Omobranchus zebra, a blennid fish.

Figure 6 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 6. Europila withersi on the leaf of mangroves.

Figure 4 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 4. Coenobita cavipes climbing on Exocearia aggolacha, mangrove.

Figure 5 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 5. Balanus amhritrite and Nerita articulata on the mangrove.

Figure 2 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 2. Mangrove area of Subarnarekha estuary at Bichitrapur.

Figure 1 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 1. Satellite Map showing the mangrove area of Bichitrapur (Courtsey: Google earth.com).

The Role of Sacks Sentence Completion Test in a Couple with Borderline Personality Disorder and Narcissistic Personality Disorder: A Case Study

Abstract Background: A voluminous amount of material has been written on individual diagnostic indicators of borderline disorder and narcissistic personality disorder from a psychopathological perspective. However, very few have explored when a narcissist and borderline personality forms a bond or a marriage link. This research aims to show that personality psychopathology is primarily interpersonal at its foundation, and it can be well reflected using Sacks sentence completion test. Methodology: A Case study of a couple with borderline personality disorder and narcissistic personality disorder from a psychiatric hospital OPD at Jaipur.  International personality disorder examination (IPDE) DSM IV, diagnostic interview schedule and Sacks sentence completion test have been administered. The couple clinical report was qualitatively interpreted using content analysis and phenomenological approach. Results indicated that couple with borderline personality disorder and narcissistic personality disorder leads to dysfunctional adult relationship. Conclusion: Lack of secure base from parental figures in childhood is reflected in terms connecting with the partner.

opencc-by-4.0Jul 2021View details →

Figure 1 in Differences in the soil seed bank of a mining area and its surroundings: a case study inserted in the Cerrado domain

Figure 1. Location of Vazante city and study area, northwest of Minas Gerais state, Brazil.

dryad36/100

Seasonality in functional connectivity: A case study with the American marten in Forillon National Park

Protected areas are essential tools for reducing global biodiversity loss. To fulfill their ecological functions, protected areas must be connected to their surroundings, a requirement that is difficult to meet in landscapes intensively disturbed by anthropogenic activities. Therefore, protecting movement corridors at the edges of protected areas is crucial, especially for species with broad habitat needs, such as the American marten (Martes americana). However, habitat selection and space use patterns are dynamic processes, so we could expect that functional connectivity would vary temporally in response to changing environmental conditions and levels of human activities. In this study, we aimed to predict the location of movement corridors for the American marten in Forillon National Park and its periphery during two contrasted periods (snow-free: May–November; snow-covered: December–April). We used "seasonal" resource selection functions to identify core areas (interpreted as circuit "nodes") and CircuitScape to delineate movement corridors between them based on the electrical circuit theory. Habitat selection patterns differed between periods, with martens avoiding open areas, high elevations, and road proximity during the snow-free period, while avoiding areas closer to secondary roads but selecting areas closer to primary roads and housing structures during the snow-covered period. Consequently, the location of movement corridors differed partially between periods; functional connectivity was favored by the presence of forest patches for both periods, while being constrained by open environments, especially during the snow-free period. Our study highlights the importance of modeling functional connectivity at fine temporal scales to provide movement corridors that fulfill the requirements of a species at each period of its annual cycle.

opencc-zeroApr 2024View details →

Datasets for "Effects of equatorial plasma bubbles on multi-GNSS signals: A case study over South China"

Post-processed airglow data for Figure 1 and source data for other Figures.

Data: Exploring the Drivers and Dynamics of Urban Waters: A Case Study of Wuhan from 1980 to 2060

The dataset comprises three main folders: "lulc," "factors," and "results." 1. lulc (Land Use/Land Cover): This folder contains raster data spanning from 1980 to 2020, representing land use and land cover dynamics. The data is stored in TIFF format, with values ranging from 1 to 6 denoting different land cover classes: farmland (1), woodland (2), grassland (3), waters (4), settlements (5), and unused land (6). 2. factors: This folder includes various driving factors influencing water dynamics. It encompasses eleven raster datasets, including Digital Elevation Models (DEM), annual precipitation data, and other relevant variables. The temporal coverage of these factors spans from 1980 to 2020. Additionally, demographic factors such as population data and economic indicators like GDP are available, covering the period from the 1990s to 2020. All datasets are stored in TIFF format with a spatial resolution of 30 meters. 3. results: This folder contains the outcomes of the analysis conducted using the PLUS (Patch Landscape and Urban Growth Model) framework. The results include outputs from the Landscape Ecological Assessment Protocol (LEAP) and the Cellular Automata for Regional Simulation (CARS) modeling. These results offer insights into the spatial and temporal patterns of water dynamics in the study area. The data structure follows a systematic organization to facilitate access and analysis. Each folder contains raster datasets stored in TIFF format, ensuring compatibility and ease of use with common GIS software. The temporal consistency of the datasets allows for longitudinal analysis of water dynamics, while the inclusion of various driving factors provides a comprehensive understanding of the underlying processes. Overall, this dataset offers valuable insights into land-water interactions and their implications for urban planning and water resource management strategies.

Dataset: Digital Transformation in Developing Countries (A Case Study)

This data represents the availability of data to support the publication of researchers' papers on digital transformation in developing countries (case study: Indonesia).

opencc-by-4.0Mar 2024View details →

Estimating Usage Of Open Source Projects - Flutter Telemetry Case Study - MSR' 24

This dataset (CSV) was assembled to support analysis within a case study that will be published in the proceedings of the <a href="https://conf.researchr.org/home/msr-2024">Mining Software Repositories</a> conference (MSR ‘24) April 14-15 2024: <a title="Estimating Usage Of Open Source Projects" href="https://doi.org/10.1145/3643991.3645066">Estimating Usage Of Open Source Projects.</a> This case study explored whether publicly available metrics could serve as proxies to estimate usage of an open source project. Using the <a href="https://flutter.dev/">Flutter</a> project as our case study, we collected monthly proxy metrics from GitHub, StackOverflow and Slack to compare with Flutter’s monthly active user count over the same time period: January 2018 through February 2021. All metrics correspond to the last day of the month and/or represent aggregate activity in that month. Data from GitHub shows aggregate activity counts across the entire <a href="https://github.com/flutter">Flutter GitHub organization</a> (up to 33 repositories).  Our specific metrics include: <div> <table> <tbody> <tr> <td> Source </td> <td> Metric </td> <td> Aggregation method </td> <td> Details </td> </tr> <tr> <td> Flutter </td> <td> Monthly Active Users (MAU) </td> <td> Google internal tooling </td> <td> Flutter users active in the last 30 days, collected on the last day of each month </td> </tr> <tr> <td> GitHub </td> <td> PullRequest Authors in month </td> <td> <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a> </td> <td> As Google governs changes to this code base, we excluded known Google employees in code change related metrics  </td> </tr> <tr> <td> GitHub </td> <td> Issues Created in month </td> <td> <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a> </td> <td> </td> </tr> <tr> <td> GitHub </td> <td> Issue Authors in month </td> <td> <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a> </td> <td> </td> </tr> <tr> <td> GitHub </td> <td> Fork events in month </td> <td> <a href="http://gharchive.org">gharchive.org</a> </td> <td> </td> </tr> <tr> <td> GitHub </td> <td> Fork cumulative count at the end of month </td> <td> <a href="http://gharchive.org">gharchive.org</a> </td> <td> </td> </tr> <tr> <td> StackOverflow </td> <td> Question Authors in month </td> <td> <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a> </td> <td> </td> </tr> <tr> <td> StackOverflow </td> <td> Questions in month </td> <td> <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a> </td> <td> </td> </tr> <tr> <td> Slack </td> <td> Claimed (cumulative) members at the end of the month </td> <td> fluttercommunity.slack </td> <td> </td> </tr> <tr> <td> Slack </td> <td> Cumulative messages at the end of the month </td> <td> fluttercommunity.slack </td> <td> </td> </tr> </tbody> </table> </div> Tools:  <ul> <li> We used an instance of <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a> hosted by <a href="https://bitergia.com/">Bitergia</a> to aggregate GitHub PullRequest Authors, GitHub Issue Authors, GitHub Issues, across all repositories under the Flutter Organization, and StackOverflow Question Authors, and StackOverflow Questions for questions that mention Flutter. We used the <a href="http://github.com/chaoss/grimoirelab-sortinghat">Sorting Hat</a> of feature GrimoireLab to identify Google employees in this sample. </li> </ul> <ul> <li> <a href="http://gharchive.org">GHArchive</a> via <a href="https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code">BigQuery</a> was used to count GitHub Star and Fork events across all repositories under the Flutter Organization </li> <li> We pulled Slack activity directly from <a href="http://fluttercommunity.slack.com">Flutter's slack channel </a>dashboard </li> </ul>

Table 2 in Altai Mountains - cradle of hybrids and introgressants: A case study in Veronica subg. Pseudolysimachium (Plantaginaceae)

Table 2. Details of AMOVA in each group with different combination without including the putative hybrid.<table><tbody><tr><th></th><th>Source of variation</th><th>Variance components</th><th>Percentage of variation</th><th>Fixation index</th></tr></tbody><tbody><tr><th>Global AMOVA based on K = 5 results of STRUCTURE including all the putative pure 174 individuals</th><td>Among Species</td><td>14</td><td>16</td><td></td></tr><tr><td>Within Species</td><td>74</td><td>84</td><td></td></tr><tr><td>TOTAL</td><td>88</td><td>100</td><td>F ST: 0.16*</td></tr><tr><th>Hierarchical AMOVA based on K = 5 results of STRUCTURE including all the putative pure 174 individuals</th><td>Among Groups</td><td>7.2</td><td>8.13</td><td>F CT: 0.08*</td></tr><tr><td>Among Species</td><td>7.1</td><td>8.01</td><td>F SC: 0.09*</td></tr><tr><td>Within Species</td><td>74.3</td><td>83.86</td><td>F ST: 0.16*</td></tr><tr><td>TOTAL</td><td>88.6</td><td>100</td><td></td></tr></tbody></table>F ST, correlation within populations relative to total; F CT, correlation within groups relative to total; F SC, correlation within populations relative to groups.* P <0.001, 10,000 permutations.

Table 1 in Altai Mountains - cradle of hybrids and introgressants: A case study in Veronica subg. Pseudolysimachium (Plantaginaceae)

Table 1. Details of sampled morphotypes and their geographical distribution.<table><tbody><tr><th>Serial no.</th><th>Species</th><th>Distribution</th><th>Latitude</th><th>Longitude</th><th>Number of individuals</th></tr></tbody><tbody><tr><th>1</th><td>V. ×altaica</td><td>Russia</td><td>50.9158</td><td>82.3274</td><td>12</td></tr><tr><th>2</th><td>V. ×grisea</td><td>Russia</td><td>50.6399</td><td>86.3131</td><td>9</td></tr><tr><th>3</th><td>V. ×kolyvanensis</td><td>Russia</td><td>51.7684</td><td>82.1381</td><td>6</td></tr><tr><th>4</th><td>V. ×sapozhnikovii</td><td>Mongolia</td><td></td><td></td><td>1</td></tr><tr><th>5</th><td>V. ×schmakovii</td><td>Russia</td><td>50.1567</td><td>88.2953</td><td>11</td></tr><tr><th>6</th><td>V. ×sessiliflora</td><td>Russia</td><td>50.3437</td><td>87.4315</td><td>13</td></tr><tr><th>7</th><td>V. ×smirnovii</td><td>Mongolia</td><td>46.3533</td><td>91.2095</td><td>6</td></tr><tr><th>8</th><td>V. arenosa</td><td>Mongolia</td><td></td><td></td><td>3</td></tr><tr><th>9a</th><td>V. incana</td><td>Russia</td><td>50.6461</td><td>86.3144</td><td>10</td></tr><tr><th>9b</th><td>V. incana</td><td>Russia</td><td>51.3924</td><td>82.2084</td><td>24</td></tr><tr><th>10</th><td>V. longifolia</td><td>Russia</td><td>53.3346</td><td>84.2004</td><td>27</td></tr><tr><th>11</th><td>V. pinnata</td><td>Russia</td><td>50.3501</td><td>87.4125</td><td>22</td></tr><tr><th>12</th><td>V. porphyriana</td><td>Russia</td><td>51.0431</td><td>85.6399</td><td>36</td></tr><tr><th>13</th><td>V. reverdattoi</td><td>Russia</td><td>50.4940</td><td>91.3311</td><td>1</td></tr><tr><th>14</th><td>V. sajanensis</td><td>Russia</td><td>56.1262</td><td>92.9057</td><td>2</td></tr><tr><th>15</th><td>V. spicata</td><td>Russia</td><td>50.3605</td><td>82.2448</td><td>37</td></tr><tr><th>16</th><td>V. spuria</td><td>Russia</td><td>51.7684</td><td>82.1381</td><td>7</td></tr><tr><th>17</th><td>V. taigischensis</td><td>Russia</td><td>53.0584</td><td>93.3399</td><td>3</td></tr><tr><th>TOTAL</th><td>17 morphotypes (10 taxonomically described pure forms; 7 taxonomically described putative hybrids forms; 3 individuals were not identified, they are listed in suppl. Table S1)</td></tr></tbody></table>

opencc-by-4.0Nov 2024View details →

Sahana et al. Supplementary Data for Global Transboundary River Research: Databases, Case Study Analysis, and Regional Statistics for Sustainable Management

This dataset supports our comprehensive review article on transboundary river research, exploring its implications for sustainable management worldwide. Utilizing machine learning, we analyzed 4,237 publications and conducted an in-depth desk review of 325 selected papers, examining a total of 4,713 case studies spanning 286 river basins globally. The study provides critical insights into upstream, midstream, and downstream regions, offering a complete view of challenges and opportunities in transboundary river management. Supplementary Data 1 contains the main database used in this study, sourced from Scopus, Web of Science, and Google Scholar. Additionally, Supplementary Data 2 and 3, included in the spreadsheet, offer statistics and further resources essential for understanding regional and cross-regional dynamics in river basin governance. These supplementary resources include key statistics, case study metadata, and tools, helping to facilitate a deeper exploration of basin-specific and global trends in transboundary water management. This collection of data and resources provides a valuable foundation for researchers and policymakers in advancing sustainable transboundary river management practices.

opencc-by-4.0Nov 2024View details →

RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"

Corresponding peer-reviewed publication This dataset consists of output files of the study reported in: <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Summary This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution. Software The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>. Study domain The files in this dataset correspond to Mississippi River Basin. Description of files All files below were prepared by Manu Tom, using the software mentioned above. 1980-01 <ul> <li>GLDAS_VIC_3H_1980-01_utc.nc4.  This netCDF file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</li> <li>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> 1980-02 <ul> <li>GLDAS_VIC_3H_1980-01_utc.nc4. This netCDF file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</li> <li>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </li> <li>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> 1980-03 <ul> <li>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29. </li> </ul> Other necessary links associated with this dataset: RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a> RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a> GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a> NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a> References David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (2011), River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a> David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a> Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., et al. (2004). The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a>

opengpl-3.0-or-laterAug 2024View details →

Digging for Decision Trees: A Case Study in Strategy Sampling and Learning (experimental reproduction package)

This artifact permits to reproduce the experimental results obtained with the techniques and algorithms presented in the article "Digging for Decision Trees: A Case Study in Strategy Sampling and Learning" by Carlos E. Budde, Pedro R. D'Argenio, and Arnd Hartmanns (2024). The contents include all data and software (formal models, software tools, Python & bash scripts) used in the experimental evaluation presented in §7 of the article. Detailed instructions on how to reproduce the results are bundled in the artifact. Execution has been tested in the Virtual Machine available at https://zenodo.org/records/7113223.

The role of spatial terms in time expressions: A case study of Chinese temporal words

This project provides the research data, namely all the Chinese temporal words collected from the BCC corpus for The role of spatial terms in time expressions: A case study of Chinese temporal words. There is one table here which includes all tokens taken from the BCC corpus. More specifically, it lists 692 unique Chinese temporal words along with their frequencies. All of these temporal words have been classified into three categories: explicitly spatialized temporal words, implicitly spatialized temporal words, and others. All explicitly spatialized temporal words have been annotated based on the type of spatial terms they are marked with, mainly including location/direction, motion, dimension, objects, deixis, and spatial mixtures.