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79 results for “integrative framework”
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry
<p>Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry. </p> <p>Dataset includes 1) model and analysis, and 2) supplemental data. </p> <p>In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file. </p> <p><strong>Acknowledgments</strong></p> <p>We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory. </p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). </p> <p><strong>Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations</strong></p> <p>Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9<br> 1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA.<br>2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA.<br>3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106<br>4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA<br>5Department of Natural Resources and the Environment, University of New Hampshire;<br>Durham, NH, USA 03824<br>6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ,<br>85721, USA<br>7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France<br>8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210<br>9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA.<br>10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA.<br>11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA.<br>12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University<br>of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia<br>13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA</p>
SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data
<p>Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA is avaiable. </p>
PEATCLSM_Trop: Integrating peat-specific land surface hydrology of natural and drained tropical peatlands in the GEOS CLSM framework
<p>The datasets archived here include simulation results shown in the peer-reviewed article “Tropical peatland hydrology simulated with a global land surface model“, published in the open access AGU Journal of Advances in Modeling Earth Systems (JAMES; Apers et al., 2022). The output was produced using the Catchment land surface model (CLSM), the land model component of the NASA Goddard Earth Observing System (GEOS) modeling framework, and various versions of peatland-specific adaptations of CLSM, i.e. PEATCLSM. Here, we provide netCDF files (*.nc or *.nc4c) for CLSM, the natural (PEATCLSM<sub>Trop,Nat</sub>), and drained (PEATCLSM<sub>Trop,Drain</sub>) tropical versions of PEATCLSM. The simulations are at a 9-km spatial resolution (EASEv2 grid) for the three major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia, using a peat grid cell distribution that is a combination of the PEATMAP distribution from Xu et al. (2018) and the peat distribution from De Lannoy et al. (2014). Simulations with the northern version of PEATCLSM (PEATCLSM<sub>North,Nat</sub>) are not included in the archived dataset but can be obtained upon request. We provide three types of netCDF files:<br> • daily_images_*.nc4c: daily land states and fluxes for variables discussed in Apers et al., (2022; Table 1), provided as netCDF image-chunked image stack;<br> • daily_mean_*.nc: 20-year mean of the land states and fluxes (Table 1), provided as a single netCDF image;<br> • daily_std_*.nc: 20-year standard deviation of the land states and fluxes (Table 1), provided as a single netCDF image.</p> <p>The file content is described in the file PEATCLSM_Trop-Simulations.pdf.</p> <p>Please contact Sebastian Apers (sebastian.apers@kuleuven.be) or Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.<br> <br> References:<br> Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., … others (2022). Tropical peatland hydrology simulated with a global land surface model. <em>Journal of Advances in Modeling Earth Systems</em>. https://doi.org/10.1029/2021MS002784<br> Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., ... others (2019). PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems, 11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574<br> De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., & Liu, Q. (2014). An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. <em>Journal of Advances in Modeling Earth Systems, 6</em>(4), 957– 979. https://doi.org/10.1002/2014MS000330<br> Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. <em>Catena, 160</em>, 134–140. https://doi.org/10.1016/j.catena.2017.09.010</p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Dataset: "Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification"
<p>The SQLite database contains the pre-computed tandem mass spectra (MS2) and retention order scores used for the experiments in the publication: "<a href="https://doi.org/10.1093/bioinformatics/btaa998">Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification</a>" by Bach et al. (2020).</p> <p>A detailed description of the database structure is given in the 'README.md' and can also be found in the <a href="https://github.com/aalto-ics-kepaco/msms_rt_score_integration/tree/master/data">code-repository associated with the publication</a>. The database layout is illustrated in the 'db_layout.png' file.</p> <p>The SQLite file 'ms_and_rt_score_DB_bach_etal_2020.db.gz' is compressed using <a href="https://en.wikipedia.org/wiki/Gzip">gzip</a>.</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
Towards suitable practices for the integration of social life cycle assessment into the ecodesign framework of hydrogen-related products
<p>The hydrogen sector is envisaged as one of the key enablers of the energy transition that the European Union is facing to accomplish its decarbonization targets. However, regarding the technologies that enable the deployment of a hydrogen economy, a growing concern exists about potential burden-shifting across sustainability<br>dimensions. In this sense, social life cycle assessment arises as a promising methodology to evaluate the social implications of hydrogen technologies along their supply chains. In the context of the European projects eGHOST and SH2E, this study seeks to advance on key methodological aspects of social life cycle assessment when it<br>comes to guiding the ecodesign of two relevant hydrogen-related products: a 5 kW solid oxide electrolysis cell stack for hydrogen production, and a 48 kW proton-exchange membrane fuel cell stack for mobility applications.<br>Based on the social life cycle assessment results for both case studies under alternative approaches, the definition of a product-specific supply chain, making use of appropriate cut-off criteria, was found to be the preferable choice when addressing system boundaries definition. Moreover, performing calculations according to the activity variable approach was found to provide valuable results in terms of social hotspots identification to support subsequent decision-making processes on ecodesign, while the direct calculation approach is foreseen as a complement to ease the interpretation of social scores. It is concluded that advancements in the formalization of<br>such suitable practices could foster the integration of social metrics into the sustainable-by-design framework of hydrogen-related products.</p>
Characterization Data for the Manuscript: "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments"
<p>This entry contains characterization data for the manuscript "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments".</p>
Data, code and supplementary material for "A data integration framework for spatial interpolation of temperature observations using climate model data"
<p>Each zipped file contains code and data to reproduce the results in the paper and supplementary material. The Cyprus folder contains also the files to run the model, as well as the associated results. The Morocco folder only contains the results and the code used to manipulate it. </p>
Data from: Ppgm: an R package for integrating neontological, palaeontological, and climate data in a phylogenetic comparative framework
Open the record for dataset details and reuse information.
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Data for: An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
<p><span>Soybean [<em>Glycine max (L.) Merr.</em>] </span><span>serves as one of the most economically valuable crops globally, but it is sensitive to low temperatures during the crop growing season. Currently, agriculture around the world has faced more serious abiotic stresses due to climate change, so there is an urgent need to breed cold-tolerant cultivars to resist the changing environment. The cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. A total of 56 soybean samples were used, including 28 resistant varieties and 28 susceptible varieties, in the field experiments. We selected 55 SNPs (which were mapped to 39 CTgenes) from the CTgenes for distinguishing cold-tolerant lines from cold-susceptible lines. The SNP data can be applied for soybean's cold-tolerant experiment, such as soybean marker-assisted selection, soybean varieties clustering, the systems biology analysis, and further validation. </span></p>
An integrated framework for analysing, simulating and testing UML models
<h1>Project Overview</h1> <p>This is the artefact associated with the paper "An integrated framework for analysing, simulating and testing UML models", submitted to the 27th Brazilian Symposium on Formal Methods (SBMF 2024).</p> <h2>Directory Structure</h2> <ul> <li><strong>CSP_Validation</strong>: Contains the validation of our mapping rules from UML diagrams to CNL requirements.</li> <li><strong>NAT2TEST_Projects</strong>: Contains the NAT2TEST projects for two case studies: the classical Dijkstra's dining philosophers problem, and a distributed ring-buffer model.</li> <li><strong>UML2CNL</strong>: Contains the implementation of our mapping rules from UML diagrams to CNL requirements.</li> </ul>
Process Integration Multi-Actor Multi-Criteria Optimization (PI-MAMCO) Framework: Case Study on Palm Oil Based Complex (POBC)
<p>The dataset includes the complete workflow for the PI-MAMCO method implementation, demonstrated on a POBC integration case study. The files are structured as follows:</p> <ol> <li> <p><strong>NOTEBOOKS Folder</strong>: This folder contains the Jupyter notebooks that constitute the entire workflow of our case study. These notebooks import necessary model equations, data, and functions from the "MODEL" folder. Running these notebooks with the correct Python package versions will reproduce the exact results presented in our submitted article.</p> </li> <li> <p><strong>MODEL Folder</strong>: This folder includes all model equations, data, and functions required by the Jupyter notebooks. The scripts in this folder are integral to executing the PI-MAMCO method as outlined in our research.</p> </li> <li> <p><strong>OUTPUTS Folder</strong>: The results generated by running the notebooks are saved in this folder. This includes all outputs reported in our manuscript as well as additional results not included in the paper.</p> </li> <li><strong>OUTPUTS_GENERATED Folder</strong>: The files contained in this folder are the expected results from executing the case study notebook.</li> </ol> <p><em>Reproducibility</em>: To ensure the reproducibility of our results, please ensure that you are using the correct versions of the required Python packages. Detailed instructions for setting up the Python environment are provided within the notebook and the readme.txt.</p> <p>Contained are also outputs from the <a href="https://www.sciencedirect.com/science/article/pii/S0957417422003931" target="_blank" rel="noopener">IRenE procedure</a>, used for the literature review (<em>01_extracted_keywords.xlsx</em> ; <em>02_sampling_results.csv</em>)</p>
RatXcan: A framework for cross-species integration of genome-wide association and gene expression data
<p>Data for paper</p> <p><span>RatXcan: A framework for cross-species integration of genome-wide association and gene expression data</span></p> <p><span>Natasha Santhanam</span><span><span>1</span></span><span><span>†</span></span><span>, Sandra Sanchez-Roige</span><span><span>2,3,4</span></span><span><span>†</span></span><span>, Sabrina Mi</span><span><span>2</span></span><span>, Yanyu Liang</span><span><span>1</span></span><span>, Apurva S. Chitre</span><span><span>2</span></span><span>, Daniel Munro</span><span><span>2</span></span><span>, Denghui Chen</span><span><span>2</span></span><span>, Riyan Cheng</span><span><span>2</span></span><span>, Jianjun Gao</span><span><span>2</span></span><span>, Angel Garcia-Martinez</span><span><span>6</span></span><span>, Anthony M. George</span><span><span>5</span></span><span>, Alexander F. Gileta</span><span><span>2</span></span><span>, Wenyan Han</span><span><span>6</span></span><span>, Katie Holl</span><span><span>7</span></span><span>, Alesa Hughson</span><span><span>8</span></span><span>, Christopher P. King</span><span><span>9</span></span><span>, Alexander C. Lamparelli</span><span><span>9</span></span><span>, Connor D. Martin</span><span><span>5</span></span><span>, Festus Nyasimi</span><span><span>1</span></span><span>, Celine L. St. Pierre</span><span><span>2</span></span><span>, Sarah Sumner</span><span><span>1</span></span><span>, Jordan Tripi</span><span><span>9</span></span><span>, Tengfei Wang</span><span><span>6</span></span><span>, Hao Chen</span><span><span>6</span></span><span>, Shelly Flagel</span><span><span>8</span></span><span>, Keita Ishiwari</span><span><span>5,10</span></span><span>, Paul Meyer</span><span><span>5,9</span></span><span>, Oksana Polesskaya</span><span><span>2</span></span><span>, Laura Saba</span><span><span>11</span></span><span>, Leah C. Solberg Woods</span><span><span>12</span></span><span>, Abraham A. Palmer</span><span><span>2,3</span></span><span>*, Hae Kyung Im</span><span><span>1</span></span><span>*</span></p> <p><span> </span></p> <p><span>[1] Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA</span></p> <p><span>[2] Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[3] Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[4] Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA</span></p> <p><span>[5] University at Buffalo, Clinical and Research Institute on Addictions University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[6] University of Tennessee Health Science Center, Department of Pharmacology, Addiction Science and Toxicology, Memphis, TN, 38120, USA</span></p> <p><span>[7] Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, 53226, USA</span></p> <p><span>[8] University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA</span></p> <p><span>[9] University at Buffalo, Department of Psychology, Buffalo, NY, 14260, USA</span></p> <p><span>[10] University at Buffalo, Pharmacology and Toxicology University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[11] University of Colorado Anschutz Medical Campus, Department of Pharmaceutical Sciences, Aurora, CO 80045, USA</span></p> <p><span>[12] Wake Forest University School of Medicine, Department of Internal Medicine, Winston-Salem, NC, 27157, USA</span></p>
Survey data of an integrative evaluation framework for assessing the sustainability of different types of urban agriculture
<p>In this dataset we present core data of an integrative evaluation framework for assessing the environmental, social, and economic sustainability of urban agriculture. The multi-criteria analysis is conducted by an Analytic Hierarchy Process and a participatory approach. The data integrate the selection and weighting of sub-criteria based on two online surveys:</p> <p>1) Survey 1: The selection of suitable sub-criteria for assessing the sustainability of urban agriculture was done by European scientific experts.</p> <p>2) Survey 2: The weighting of the selected sub-criteria was done on the example of vertical farming and community supported agriculture. Therefore, we involved stakeholders representing key actors for the implementation of urban agriculture: city administrations and non-governmental organizations (NGOs) of ten German case study cities, practitioners and technical-scientific experts.</p> <p> </p> <p><strong>List of data and content</strong></p> <p>1) Survey_1 (*.zip):</p> <ul> <li>Survey_1_Criteria_Selection_English: Online survey in English (*.pdf)</li> <li>Survey_1_Information_Sub-criteria_English: Information about the sub-criteria provided in the survey (in English) (*.pdf)</li> <li>Survey_1_Groups: Results of the statistical analyses (U-tests and Kruscal-Wallis) to detect group-specific differences (e.g. gender, different length or degree of experience with urban agriculture, scientific focus, target group, expertise); the tests were conducted with IBM SPSS Statistics 25 (*.xlsx)</li> </ul> <p>2) Survey_2 (*.zip):</p> <ul> <li>Survey_2_AHP_City_Administrations_German: Online survey for city administrations in German (*.pdf)</li> <li>Survey_2_AHP_Practitioners_German: Online survey for practitioners and technical-scientific experts in German (*.pdf)</li> <li>Survey_2_AHP_NGOs_German: Online survey for NGOs in German (*.pdf)</li> <li>Survey_2_Information_Sub-Criteria_German: Information about the sub-criteria provided in the survey (in German) (*.pdf)</li> <li>Survey_2_Groups: Results of the statistical analyses (U-tests and Kruscal-Wallis) to detect group-specific differences (e.g. gender, different length or degree of experience with urban agriculture, scientific focus, target group, expertise); the tests were conducted with IBM SPSS Statistics 25 (*.xlsx)</li> <li>rdata_CA_AHP_edible_Cities_2022-03-18_10-28: Results of the survey for city administrations (*.csv)</li> <li>rdata_NGO_AHP_edible_Cities_2022-03-18_10-40: Results of the survey for NGOs (*.csv)</li> <li>rdata_PE_AHP_edible_Cities_2022-03-18_10-41: Results of the survey for practitioners and technical-scientific experts (*.csv)</li> <li>rdata_all_AHP_edible_Cities_2022-03-18_09-53: Total results of the survey</li> </ul> <p> </p> <p><strong>Data acquisition and processing</strong></p> <p>The methods are described in this linked publication:</p> <p><span>John, H., & Artmann, M. (2024). Introducing an integrative evaluation framework for assessing the sustainability of different types of urban agriculture. </span><span>I<em>nternational Journal of Urban Sustainable Development, </em></span>16 (1), 35-52<em><span>. </span></em><span>doi:<em> </em>10.1080/19463138.2024.2317795</span></p> <p>The methodology of the performed analytic hierarchy process (AHP) is published in a separate repository on GitHub including a paper that systematically explains the AHP by means of code examples, starting with the raw data, through their adaptation to the software functions of the ahpsurvey R-package, and finally, execution of the AHP up to the visualization of the results.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors thank Mabel Killinger and Marie Herzig for their help in stakeholder identification as well as all experts and stakeholders for their participation in the two online surveys and their helpful comments. Data processing and analysis by means of an Analytic Hierarchy Process in R would not have been possible without the help of Björn Kasper.</p>
Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts
<p>Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts</p>
ScienceDex guides
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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.