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3,225 results for “Case of study”
Figure 6 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping
Figure 6. – Cognitive maps of threats of fishing practices on the fish resource and environment.
Figure 8 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping
Figure 8. – Potential chains of events caused by the Westernization process.
FIGURE 3 in Anthropologically introduced biases in natural history collections, with a case study on the invertebrate paleontology collections from the middle Cambrian Spence Shale Lagerstätte
FIGURE 3. Percentage of taxonomic groups in the KUMIP Spence Shale collection by specimen counts.
Improving Volcanic SO2 Cloud Modeling Through Data Fusion and Trajectory Analysis: A Case Study of 2022 Hunga Tonga Eruption
<p><strong>Dataset Overview</strong>: This dataset comprises approximately 500 clusters of aggregated observational data collected from January 16 to 20 during the ascending (ASC) and descending (DES) periods. We grouped a large number of observation points into these clusters and calculated trajectories from the center of each cluster. The choice of 500 clusters was driven by pragmatic considerations, aiming for a balance between computational feasibility and the level of detail needed for our analysis.</p> <p><strong>Data Unit Description</strong>: The "mass" values in this dataset for each cluster are calculated by multiplying the mass per unit area (<span><span>g/m2</span></span>) of individual data points by the area covered by each point, thus providing the total mass in grams (g). The "heights" are presented in units of kilometers (km), representing the observed top heights of each cluster.</p>
Figure 3 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 3. Flow diagram for calculation of WF in Van province.
Figure 2 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 2. Monthly average air temperature, total and effective precipitation values in Van.
Figure 8 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 8. Distribution of WF , and WF by years in Van province.
Figure 1 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 1. The study area.
Figure 4 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 4. Distribution of WF in Van province.
Figure 9 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 9. Distribution of WF in Van province when feed crops total are included in WF .
Fig. 8 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
Fig. 8 - Shells index of roundness H/D of the five samples.
Fig. 6 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
Fig. 6 - Shell measurements (drawn by Dael Sassoon).
Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case)
<p>Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case). The first GWAS meta-analysis combines GWAS summary statistics of invasive melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The second GWAS meta-analysis combines GWAS summary statistics of in situ melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The third GWAS meta-analysis combines GWAS summary statistics of in situ vs invasive (case-case; in situ code 0, invasive code 1) melanoma from UK Biobank (as of August 2022), QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>Columns</p> <p>CHR Chromosome</p> <p>SNP rsid</p> <p>POS Base position HG Build 37</p> <p>A1 effect allele</p> <p>A2 Non-effect allele</p> <p>A1FREQ Allele frequency of effect allele</p> <p>BETA effect estimate of effect allele</p> <p>SE standard error of effect estimate</p> <p>PVAL two-tailed p value</p> <p>DIRECTION the Direction of effect of the SNP in each cohort ( in the order UKBB, FINNGEN, QSKIN, QMEGA 610k, QMEGA OMNI)</p> <p>N sample size</p> <p>See </p>
Fig. 1 in Refining the Occurrence of Viral Encephalopathy and Retinopathy, Photobacteriosis, and Vibriosis in Connection with Seawater Physicochemical Parameters: A Five-Year Case Study Abstract
Fig. 1: Study area.
Figure 2 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 2. Results of Cluster Analysis.
Figure 1 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 1. Rusałka Lake in Szczecin City, own elaboration, after Poleszczuk et al. (2012).
Figure 3 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 3. Results of discriminant analysis.
Diversity's Role in Collaboration and Conflict: A Case-Study of React.js
Open the record for dataset details and reuse information.
A case study on the impact of interplanetary coronal mass ejection on the Martian O(1S) 557.7 nm dayglow emission using ExoMars TGO/NOMAD-UVIS observations: First Results
<p>This study presents the impact of interplanetary coronal mass ejection (ICME) on the O(1S) 557.7 nm dayglow emission in the Martian atmosphere using instruments onboard ExoMars-TGO and MAVEN spacecrafts.</p>
Methodology data of "A qualitative and quantitative citation analysis toward retracted articles: a case of study"
<p>This document contains the datasets and visualizations generated after the application of the methodology defined in our work: <em>"A qualitative and quantitative citation analysis toward retracted articles: a case of study"</em>. The methodology defines a citation analysis of the Wakefield et al. [1] retracted article from a quantitative and qualitative point of view. The data contained in this repository are based on the first two steps of the methodology. The first step of the methodology (i.e. “Data gathering”) builds an annotated dataset of the citing entities, this step is largely discussed also in [2]. The second step (i.e. "Topic Modelling") runs a topic modeling analysis on the textual features contained in the dataset generated by the first step. </p> <p><strong>Note:</strong> the data are all contained inside the "<strong><em>method_data.zip"</em> </strong>file. You need to unzip the file to get access to all the files and directories listed below.</p> <p> </p> <p><strong>Data gathering</strong></p> <p>The data generated by this step are stored in <strong>"<em>data/</em>"</strong>:</p> <ol> <li><em><strong>"cits_features.csv": </strong></em>a dataset containing all the entities (rows in the CSV) which have cited the Wakefield et al. retracted article, and a set of features characterizing each citing entity (columns in the CSV). The features included are: DOI ("doi"), year of publication ("year"), the title ("title"), the venue identifier ("source_id"), the title of the venue ("source_title"), yes/no value in case the entity is retracted as well ("retracted"), the subject area ("area"), the subject category ("category"), the sections of the in-text citations ("intext_citation.section"), the value of the reference pointer ("intext_citation.pointer"), the in-text citation function ("intext_citation.intent"), the in-text citation perceived sentiment ("intext_citation.sentiment"), and a yes/no value to denote whether the in-text citation context mentions the retraction of the cited entity ("intext_citation.section.ret_mention").<br> <strong>Note: </strong>this dataset is licensed under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">Creative Commons public domain dedication (CC0)</a>.<br> </li> <li><em><strong>"cits_text.csv": </strong>this dataset stores the abstract ("abstract") and the in-text citations context ("intext_citation.context") </em>for each citing entity identified using the DOI value ("doi").<br> <strong>Note: </strong>the data keep their original license (the one provided by their publisher). This dataset is provided in order to favor the reproducibility of the results obtained in our work.</li> </ol> <p> </p> <p><strong>Topic modeling</strong><br> We run a topic modeling analysis on the textual features gathered (i.e. abstracts and citation contexts). The results are stored inside the <em><strong>"topic_modeling/"</strong></em> directory. The topic modeling has been done using MITAO, a tool for mashing up automatic text analysis tools, and creating a completely customizable visual workflow [3]. The topic modeling results for each textual feature are separated into two different folders, <em><strong>"abstracts/"</strong></em> for the abstracts, and <em><strong>"intext_cit/"</strong></em> for the in-text citation contexts. Both the directories contain the following directories/files: <strong> </strong></p> <ol> <li> <p><em><strong>"mitao_workflows/"</strong></em>: the workflows of MITAO. These are JSON files that could be reloaded in MITAO to reproduce the results following the same workflows.</p> </li> <li> <p><em><strong>"corpus_and_dictionary/": </strong></em>it contains the dictionary and the vectorized corpus given as inputs for the LDA topic modeling.</p> </li> <li> <p><em><strong>"coherence/coherence.csv":</strong></em> the coherence score of several topic models trained on a number of topics from 1 - 40.</p> </li> <li> <p><em><strong>"datasets_and_views/": </strong></em>the datasets and visualizations generated using MITAO. </p> </li> </ol> <p> </p> <p><strong>References</strong></p> <ol> <li>Wakefield, A., Murch, S., Anthony, A., Linnell, J., Casson, D., Malik, M., Berelowitz, M., Dhillon, A., Thomson, M., Harvey, P., Valentine, A., Davies, S., & Walker-Smith, J. (1998). RETRACTED: Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. <em>The Lancet</em>, <em>351</em>(9103), 637–641. <a href="https://doi.org/10.1016/S0140-6736(97)11096-0">https://doi.org/10.1016/S0140-6736(97)11096-0</a></li> <li> <p>Heibi, I., & Peroni, S. (2020). A methodology for gathering and annotating the raw-data/characteristics of the documents citing a retracted article v1 (protocols.io.bdc4i2yw) [Data set]. In protocols.io. ZappyLab, Inc. <a href="https://doi.org/10.17504/protocols.io.bdc4i2yw">https://doi.org/10.17504/protocols.io.bdc4i2yw</a></p> </li> <li> <p> </p> Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. <a href="https://doi.org/10.19245/25.05.pij.5.2.3">https://doi.org/10.19245/25.05.pij.5.2.3</a> <p> </p> </li> </ol>
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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.