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).

Example configurations and test cases for the Python HDF5Translator framework.

This is a set of use examples for the <a href="https://github.com/BAMresearch/HDF5Translator">HDF5Translator framework</a>. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does<a href="https://lookingatnothing.com/?p=4087">, there is a blog post here. </a> The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each.  We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set.

Turbid glacial runoff plumes in Kachemak Bay, Alaska: Outlines in shapefile format and spectral bands for an example case

<h2>Plume outlines</h2> The .zip files contain shapefiles of sediment plume outlines in Kachemak Bay, Alaska:  <ul> <li>shapes_high_S2.zip: High turbidity plumes originating at the head of the Bay and the input points of Portlock Creek, Grewingk Creek, and the Wosnesenski River as mapped from Sentinel-2 imagery.</li> <li>shapes_vhigh_S2.zip: Very high turbidity plumes originating at the head of the Bay and the input points of Portlock Creek, Grewingk Creek, and the Wosnesenski River as mapped from Sentinel-2 imagery.</li> <li>shapes_high_LS.zip: High turbidity plumes originating at the head of the Bay and the input points of Portlock Creek, Grewingk Creek, and the Wosnesenski River as mapped from Landsat imagery. The filenames of the shapefiles indicate which Landsat sensor (LS 4, 5, 7, or 8) the outlines were mapped from.</li> <li>shapes_vhigh_LS.zip: Very high turbidity plumes originating at the head of the Bay and the input points of Portlock Creek, Grewingk Creek, and the Wosnesenski River as mapped from Landsat imagery. The filenames of the shapefiles indicate which Landsat sensor (LS 4, 5, 7, or 8) the outlines were mapped from.</li> </ul> <h2>Grewingk Creek Plume, 11 June 2020</h2> The .tif files contain convolved spectral bands derived from an hyperspectral image acquired on 11 June 2020, and an abundance map glacial turbdity in the Grewingk Plume. The spectral bands were convolved based on the Landsat-8 OLI spectral response function.

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.

Carotenoid oil extraction efficiencies in the case of Nannochloropsis gaditana S4 strain

Carotenoids and astaxanthin (Ax) Extraction efficiency achieved by oil extraction from Nannochloropsis gaditana S4 strain grown at A4F facilities. Carotenoids and astaxanthin concentration was determined by spectrophotometric analysis

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.

INTELLIMAN_WP2_Application Requirements and Integration_T2.4_Fresh food handling use case analysis, integration and validation_Apple 6D pose estimation dataset_v0

The dataset contains the data generated for the training of the 6D pose estimation neural network DOPE related to the publication: M. Costanzo, M. De Simone, S. Federico, C. Natale and S. Pirozzi, "Enhanced 6D Pose Estimation for Robotic Fruit Picking," 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Rome, Italy, 2023, pp. 901-906, doi: 10.1109/CoDIT58514.2023.10284072.

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.

opencc-by-4.0Sep 2021View details →

Raw results of the simulation experiments performed to evaluate the Time-to-idle Control Variate ferformance in the single queue case

This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation  results presented in [1]. [1] Suárez-González A., López-García C., López-Ardao J.C., Rodríguez Rubio R., Rodríguez Pérez M. (2021). "Time-to-idle Control Variate Performance in the Single Queue Case". Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 147-151. DOI: https://doi.org/10.46354/i3m.2021.emss.020

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>