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Hydrogen Modelling Pathways - Training Case
<p>This training material includes the baseline energy system model and the techno-economic data needed to create a simplified hydrogen supply-demand chain using MUIO (Modelling User Interface for OSeMOSYS).</p>
Effective treatment of PTSD by individualistic homoeopathic treatment- a case report
<p>Dr. jaydebi Sarkar & Dr. Sandip Samanta. (2023). Effective treatment of PTSD by individualistic homoeopathic treatment: a case report. <em>Internation Journal Of Advance Research And Innovative Ideas In Education</em>, 9(6), 121-128</p>
Sahana et al. Supplementary Data for Global Transboundary River Research: Databases, Case Study Analysis, and Regional Statistics for Sustainable Management
<p><span>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.</span></p>
Prediction of COVID-19 case numbers using state-space modeling and wastewater virus datasets
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RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <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> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>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.<strong><br></strong></p> <p><strong>Software</strong></p> <p>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>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> 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><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>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><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. 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> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> 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><em>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.</em></li> <li><em>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. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. 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> <p>1980-03</p> <ul> <li><em>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.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>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></p> <p>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></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>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></p> <p>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></p> <p>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></p>
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
Unlocking born-digital literary heritage: The case of the Herman de Coninck floppy disks
<p>Datasets produced in the context of the research scenario ‘<a href="https://clariahvl.hypotheses.org/tag/unlocking-born-digital-literary-heritage">Unlocking born-digital literary heritage: the case of the Herman de Coninck floppy disks’</a><strong> </strong>in CLARIAH-VL. The aim of the research scenario was to partially ‘unlock’ the digital files stored on the floppy disks in <a href="https://collectie.letterenhuis.be/ark:/24970/37a52c26-91da-5849-bccb-9cd6b012537d">Herman de Coninck's archive</a> in the collection of the <a href="https://letterenhuis.be">Letterenhuis</a> (Antwerp) by providing insights into their contents, grouping related files and linking the files with related files in the paper archive.</p> <p>Two datasets are provided: a list of all the persons and places mentioned in the files stored on the floppy disks (excluding De Coninck’s personal communication) and a list of documents in De Coninck's archive related to the “De vliegende keeper”-columns. The list of persons and places alphabetically lists all the persons and places identified in the files using NER, their frequencies and the file in which this person or place appears (note that the list may still contain errors and duplicates). The list with "De vliegende keeper"-columns lists the title of the column and indicates the file on the floppy disks that holds a version of this column, whether a version of this column is can be found as printout or as newspaper clipping in the paper archive (C33941/323, C33941/324, C33941/325) and on which page of the collection of essays <em>De vliegende keeper</em> (1995) this essay appears.</p> <p>The <a href="https://clariahvl.hypotheses.org/tag/unlocking-born-digital-literary-heritage">blog posts of this research scenario</a> on CLARIAH-VL's website provide more information on the creation of the datasets.</p> <p>After approval, the files from the floppy disks (C339941/74) can be accessed at the Letterenhuis for further research. </p>
Simulation cases in the manuscript
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Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Digging for Decision Trees: A Case Study in Strategy Sampling and Learning (experimental reproduction package)
<p>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).</p> <p>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.</p> <p> </p>
The role of spatial terms in time expressions: A case study of Chinese temporal words
<p>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.</p>
Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China
<p>This version updates the dataset to reflect revised analyses in the resubmitted manuscript to Ecology and Evolution.</p> <p><strong>Title:</strong><br><strong>Data and Code for "Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China"</strong></p> <p><strong>Description:</strong><br>This dataset contains the raw data and R scripts used to generate specific figures in the manuscript <em>"Wildlife Diversity and the Impact of Human Disturbance on Activity Patterns: A Case Study of the Labagoumen Nature Reserve, Beijing, China,"</em> submitted to <em>Ecology and Evolution</em>. The data were collected from infrared camera traps deployed in the Labagoumen Nature Reserve, Beijing, China, between 2019 and 2022. The dataset includes species detection records, timestamps, and information relevant to assessing the effects of human disturbance on wildlife activity patterns.</p> <p>The accompanying R scripts facilitate data processing, statistical analyses, and figure generation, enabling reproduction of the key results. This dataset may be useful for researchers interested in spatiotemporal wildlife monitoring, human-wildlife interactions, and disturbance ecology.</p> <p><strong>Contents:</strong></p> <ul> <li>raw data (Excel format, <code>.xlsx</code>)</li> <li>R scripts for data analysis and figure generation of <strong>Figures 3-5</strong> (from the raw data file "raw data.xlsx") (Text format, .doc).</li> <li> <p><strong>Figure 1</strong>: Created using <span>ArcMap 10.8 software</span> and Photoshop software. <span>ArcMap 10.8 software </span>was used for processing spatial data and map creation, while Photoshop was used for post-processing and image enhancement.</p> </li> <li> <p><strong>Figure 2 and Appendix Figure. A1</strong>: Designed and modified using Photoshop software.</p> </li> </ul>
Validation of MUISCA for Blockchain Interoperability: A Case Study in a Healthcare Environment
<p>This repository contains the questions asked to the 31 experts who participated in the study “Validation of MUISCA for Blockchain Interoperability: A Case Study in a Healthcare Environment”. It also contains the spreadsheet with the recorded answers.</p>
Data for: Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi
<p>Bremer, L.L. (1,2), McGuire, G. (3,4), DeMaagd, N. (1,5), Trauernicht, C. (5)</p> <p><strong> </strong></p> <p>1 University of Hawaiʻi Economic Research Organization, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>2 Water Resources Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>3 Department of Geography and Environment, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>4 Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720</p> <p>5 Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p> </p> <p><strong>Bremer, L.L., McGuire, G., Hastings Silao, Z., Kurashima, N., Ticktin, T., Crow, S.E., Giardina, P.C., Winter, K.B., DeMaagd, N., and C. Trauernicht. Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi</strong></p> <p> </p> <p>Multi-strata agroforestry land use scenarios were created using data from the: 2020 State of Hawaiʻi Agricultural Baseline (Perroy and Collier 2020), the Hawaiʻi Carbon Assessment (Jacobi et al. 2017), current (Giambelluca et al. 2013) and RCP 8.5 mid-century projected (Ellison-Timm et al. 2015) rainfall rasters, state land use zoning (SLUC, 2020), slope, elevation, and historical colluvial agroforestry maps (Kurashima et al. 2019). The following rasters display multi-strata agroforestry land-use scenarios. </p> <ul> <li> <p>“MS_currentclimate_landuse_scenario” represents potential multi-strata agroforestry under current rainfall.</p> </li> <li> <p>“MS_RCP85_midcentury_landuse_scenario” represents potential multi-strata agroforestry under projected RCP 8.5 mid-century rainfall. </p> </li> </ul> <p>For both scenarios: 1 = dry (550-1500 mm) multistrata agroforestry; 2 = mesic (1500-3000 mm) multi-strata agroforestry; 3 = wet (>3000 mm) multi-strata agroforestry.</p> <p>Estimates of projected changes in above-ground carbon were estimated by comparing modeled AGC in agroforestry scenarios to baseline AGC for current forest (Asner et al. 2016) and estimates of AGC in non-forest vegetation (Selmants et al. 2017). The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid century rainfall.</p> <ul> <li> <p>“AGC_change_currentclimate”</p> </li> <li> <p>“AGC_change_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: -2 = significant decrease (projected maximum < baseline); -1 = trend decrease (projected maximum > baseline > projected mean; 1 = weak increase (projected minimum < baseline < projected mean); 2 = strong increase (projected minimum > baseline).</p> <p>Estimates of projected changes in soil carbon under each scenario were estimated using global meta-analyses (Cardinael et al. 2018; Chaterjee et al. 2018; De Stefano and Jacobson 2017), studies of multi-strata agroforestry transitions in similar climates and soil types, and land-use change studies in Hawaiʻi. The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid-century rainfall. </p> <ul> <li> <p>“SoilC_currentclimate”</p> </li> <li> <p>“SoilC_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios, 1 = increase low confidence; 10 = increase medium confidence; 100 = increase high confidence; 2 = no change low confidence; 20 = no change medium confidence; 200 = no change high confidence; 3 =unclear (insufficient data); 4 = unclear (mixed evidence).</p> <p>Projected synergies and tradeoffs in AGC and soil C under each scenario were estimated by combining the soil C and AGC results. The following rasters display projected synergies and tradeoffs in soil C and AGC under current and RCP 8.5 mid-century rainfall. </p> <ul> <li> <p>“AGC_Soil_Bivariate_currentclimate”</p> </li> <li> <p>“AGC_Soil_Bivariate_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: the first value is soil C category: 7 = unknown/uncertain; 8= increase; 9= no change; and the second value is AGC category: 0 = decrease; 1 = no change; 2 = increase.</p> <p><strong> </strong></p> <h2>References:</h2> <p>Asner, G. P., Sousan, S., Knapp, D. E., Selmants, P. C., Martin, R. E., Hughes, R. F., & Giardina, C. P. (2016). Rapid forest carbon assessments of oceanic islands: A case study of the Hawaiian archipelago. 11(1). <a href="https://doi.org/10.1186/s13021-015-0043-4">https://doi.org/10.1186/s13021-015-0043-4</a></p> <p>Cardinael, R., Umulisa, V., Toudert, A., Olivier, A., Bockel, L., & Bernoux, M. (2018). Revisiting IPCC Tier 1 coefficients for soil organic and biomass carbon storage in agroforestry systems. Environmental Research Letters, 13. <a href="https://doi.org/10.1088/1748-9326/aaeb5f/meta">https://doi.org/10.1088/1748-9326/aaeb5f/meta</a></p> <p>Chaterjee, N., Nair, P. K. R., Chakraborty, S., & Nair, V. D. (2018). Changes in soil carbon stocks across the forest-agrofoest-agriculture/pasture continuum in various agroecological regions: A meta-analysis. Agriculture, Ecosystems & Environment, 266, 55–67. <a href="https://doi.org/10.1016/j.agee.2018.07.014">https://doi.org/10.1016/j.agee.2018.07.014</a></p> <p>De Stefano, A., & Jacobson, M. G. (2017). soil carbon sequestration in agroforestry systems: A meta-analysis. Agroforestry Systems. <a href="https://doi.org/10.1007/s10457-017-0147-9">https://doi.org/10.1007/s10457-017-0147-9</a></p> <p>Elison Timm, O., Giambelluca, T. W., & Diaz, H. F. (2015). Statistical downscaling of rainfall changes in Hawaiʻi based on the CMIP5 global model projections. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1002/2014JD22059">https://doi.org/10.1002/2014JD22059</a></p> <p>Giambelluca, T. W., Chen, Q., Frazier, A. G., Price, J. P., Chen, Y. L., Chu, P. S., Eischeid, J. K., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawaiʻi. Bulletin Of the American Meteorological Society, 94, 313–316. <a href="https://doi.org/10.1175/BAMS-D-11-00228.1">https://doi.org/10.1175/BAMS-D-11-00228.1</a></p> <p>Jacobi, J. D., Price, J. P., Fortini, L. B., Gon III, S. M., & Berkowitz, P. (2017). Carbon Assessment of Hawaiʻi Land Cover Map [Map]. USGS. </p> <p><a href="https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b">https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b</a></p> <p>Kurashima, N., Fortini, L., & Ticktin, T. (2019). The potential of indigenous agricultural food production under climate change in Hawaiʻi. Nature Sustainability. <a href="https://doi.org/10.1038/s41892-019-0226-1">https://doi.org/10.1038/s41892-019-0226-1</a></p> <p>Selmants, P. C., Giardina, C. P., Sousan, S., Knapp, D. E., Kimball, H., Hawbaker, T. J., Moreno, A., Seirer, J., Running, S. W., Miura, T., Bergstrom, R., Hughes, R. F., Litton, C. M., & Asner, G. P. (2017). Baseline Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawaiʻi. USGS.</p> <p>State Land Use Commission. (2020). State Land Use District Boundaries [Map]. Hawaiʻi Statewide GIS Program.</p>
Application Case 3: Data sets consisting of 3D scans, 3D model and the derived metadata, from two different software programs
<p>In this repository we provide 3D scan projects and the 3D models processed from them with their metadata using the example of a wood sample. The metadata was generated using our metadata generation script, which is described in the referenced publication.</p> <p>The 3D scan projects were created in different software (atos v6.2, atos 2016 and zeiss 2023). For each there is a scan project, a 3D model and the generated metadata with and without uri in this repository.</p> <p>The publication in which this application case is included: Homburg, T., Cramer, A., Raddatz, L. <em>et al.</em> Metadata schema and ontology for capturing and processing of 3D cultural heritage objects. <em>Herit Sci</em> <strong>9</strong>, 91 (2021). <a href="https://doi.org/10.1186/s40494-021-00561-w">https://doi.org/10.1186/s40494-021-00561-w</a></p> <p>Python scripts for exporting metadata can be found here: <a href="https://github.com/i3mainz/3dcap-md-gen/tree/0.1.3">GitHub - i3mainz/3dcap-md-gen</a></p>
ANIMA - Ljubljana Airport Case Study
<p>This dataset contains the results of two surveys, performed in Slovenia to gain the participants ’evaluation of workshops on“ Transparent noise management and community engagement in the Ljubljana airport area ”. Additional informations on these surveys may be found at <a href="https://www.nijz.si/anima">https://www.nijz.si/anima</a>.</p> <p>This evaluation has been supported by the NIJZ as part of the activities in the EU Horizon 2020 ANIMA Project. For more information about this case study and the results, <a href="https://doi.org/10.5281/zenodo.5604651">see the ANIMA project D2.11 deliverable</a>.</p> <p>More information on the ANIMA project can be found on the project website: <a href="https://anima-project.eu/">https://anima-project.eu/</a></p> <p><strong>For more information:</strong><br> Sara Rahne [ info@nijz.si ]</p>
ANIMA - Rotterdam The Hague Airport, Case Study 2021
<p>This dataset contains the results from the route evaluation that took place for Rotterdam The Hague Airport in 2021, in collaboration with the working group of the CRO to evaluate an alternative starting procedure for runway 06.</p> <p>This evaluation has been supported by the Royal NLR as part of the activities in the EU Horizon 2020 ANIMA Project. For more information about this case study and the results, <a href="https://doi.org/10.5281/zenodo.5604651">see the ANIMA project D2.11 deliverable</a>.</p> <p>More information on the ANIMA project can be found on the project website: <a href="https://anima-project.eu/">https://anima-project.eu/</a></p> <p><strong>Description of data</strong></p> <ul> <li>The PDF file is a presentation of the case study during the ANIMA Workshop in Vienna, Austria in June 2021. The presentation shows the locations that were measured. </li> <li>The spreadsheet contains the raw data and a translation to scoring per location by the participants. The test generated a score (Column A) that had to be reported. To prevent bias in ordering, some participants heard the original route first and then the alternative route or the other way around. This was translated to a version number in column 2. For each location, a annoyance score is reported between 1 (not annoying) and 7 (extremely annoying).</li> <li>Averages and SD are calculated in addition to this raw data. The scoring is translated into a interval between -3 and 3 that was used for reporting.</li> </ul> <p><strong>For more information:</strong><br> Roalt Aalmoes [ roalt.aalmoes (at) nlr.nlr ]<br> </p>
Do nest boxes breed the target species or its competitors? A case study of a critically endangered bird
<p>Nest boxes are widely used for habitat restoration. Unfortunately, competitors of the target species may exploit nest boxes, creating perverse outcomes. Avoiding habitats preferred by nontarget species, while favoring those of the target species, requires an adaptive management approach if limited information about species preferences is available when deploying boxes. Using nest boxes intended for Swift Parrots Lathamus discolor, we identify factors associated with nontarget species occupancy (Common Starling Sturnus vulgaris and Tree Martin Petrochelidon nigricans) in newly deployed boxes in 2016, and then again after 3 years had elapsed in 2019. Box occupancy by different species depended on the interaction between distance of individual boxes to the forest edge and year. Although the target species exploited similar numbers of nest boxes in both years, competitors were the main beneficiaries of established boxes. A subordinate native nest competitor increased box occupancy likelihood at greater distances from forest edges in both years, but the relationship was stronger in 2019. Introduced Common Starlings S. vulgaris were most likely to occupy boxes close to forest edges, but the magnitude of this relationship was much greater for established than newly deployed boxes. We suggest that permanent box deployments for Swift Parrots may produce perverse outcomes by increasing nesting habitat for Common Starlings. We suggest that for species that only use cavities for part of their life cycle, managers should limit access to boxes outside of critical times to reduce the likelihood that pest populations can exploit restoration efforts and create new problems.</p>
Covid 19 Dataset, Cases by Country, Vaccination, GDP and Average Temperature
<p>Dataset del covid-19 obtenido mediante técnicas de web Scrapping</p>
Developing National Identity Scale: As Indonesian Case
<p><strong><em>Background: </em></strong></p> <p><em>National identity is an important concept to be developed, so it is necessary to have a measurement instrument with evidence of validity and reliability. Thus, the accuracy of the information obtained through the measurement of this instrument will be guaranteed.</em></p> <p> </p> <p><strong><em>Objective:</em></strong></p> <p><em>This article aims to test the psychometric properties of the national identity scale to fulfil the standardization of good measurement.</em></p> <p> </p> <p><strong><em>Method:</em></strong></p> <p><em>In this study, three analytical approaches were used: content validity involving eight subject matter experts (SME), confirmatory factor analysis (CFA), and reliability analysis. For the reliability and CFA approach, 300 students were used as samples taken through the convenience sampling technique.</em></p> <p> </p> <p><strong><em>Result:</em></strong></p> <p><em>The analysis results show that out of the 20 items compiled in the content validity analysis, one item is inadequate because it has a below standard score on Aiken's coefficient of validity. This one item was revised again to be included in the tryout and data collection for the following analysis stage. The results of the CFA analysis show that the model and data are fit, and there are two inadequate items. At the same time, the contribution of the three factors on the national identity scale is significant. In the third analysis, it was found that adequate coefficient reliability affected the low standard error measurement (SEM).</em></p> <p> </p> <p><strong><em>Conclusion:</em></strong></p> <p><em>Based on the analysis results, it can be concluded that the Indonesian national identity scale has fulfilled good psychometric properties. It can be proven from evidence validity based on content, confirmatory factor analysis (CFA), and reliability analysis.</em></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.