Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
115
datasets available to search
ShareScore release 0.7.1
Dataset results
115 results for “qualitative analysis”
Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)
<p>Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.</p>
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Artifact for the EASE 2020 Paper: How Can I Contribute? A Qualitative Analysis of Community Websites of 25 Unix-Like Distributions
<p>Artifact for the EASE 2020 Paper: How Can I Contribute? A Qualitative Analysis of Community Websites of 25 Unix-Like Distributions</p> <p>Jacob Krüger, Sebastian Nielebock, Robert Heumüller</p> <p> </p> <p>Please refer to the readme for more details</p>
Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis
<p>Vignettes and COREQ checklist for Applied Partnership Award project: Resource Allocation, Priority-Setting and Consensus in Dementia Care. See Keogh F, Pierse T, O'Shea E <em>et al.</em> Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis [version 1; peer review: awaiting peer review]. <em>HRB Open Res</em> 2020, <strong>3</strong>:69 (<a href="https://doi.org/10.12688/hrbopenres.13147.1">https://doi.org/10.12688/hrbopenres.13147.1</a>)</p>
When Do Authoritarian Regimes Use Digital Technologies for Covert Repression? A Qualitative Comparative Analysis (QCA) of Politico-Economic Conditions
<p>This is a replication dataset together with the QCA script used for the analysis of the article "When Do Authoritarian Regimes Use Digital Technologies for Covert Repression? A Qualitative Comparative Analysis (QCA) of Politico-Economic Conditions" submitted to the Swiss Political Science Review in 2024, for the special issue "Re-Authoritarianisation with Digital Means? Recent Developments in Digital Politics in East Central Europe and Central Asia". The package contains the following elements:</p> <p>1) The original QCA dataset with references.</p> <p>2) The supplementary dataset with the calculations related to autocratic linkages.</p> <p>3) The R script used for the QCA.</p> <p> </p>
Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using 1H NMR
<p>Dataset to accompany the manuscript "Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using <sup>1</sup>H NMR"</p>
Towards Collaborative Immersive Qualitative Analysis
<p>Figures for a published conference article in the 15th International Conference on Computer Supported Collaborative Learning, June 2022.</p> <ul> <li>Figure 1 - Screenshot of a CAVA360VR session R1 (left) and R2 (right)</li> <li>Figure 2 - Aerial view of participants and camera posi</li> <li>Figure 3 - Student group collaboration represented by a comic transcript</li> <li>Figure 4 - Bringing up an additional camera view to accomplish an alternative viewing</li> <li>Figure 5 - Drawing as a way of highlighting and creating shared awareness</li> </ul>
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Figure 1. (a) Classical set and (b) Fuzzy set 2.3.-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>A membership function is a curve that represents the degree of points which belong to the<br> specific fuzzy variable. Selecting the appropriate membership function plays an essential rule in<br> design of a fuzzy logic controller. The shape of membership function could be defined based on the<br> simplicity, convenience, speed and efficiency. Many different membership functions are introduced<br> in the literatures such as triangular, trapezoidal and Gaussian. The membership function which<br> represented in figure 1(b) is a trapezoidal type.</p>
Figure 2. Causal loop diagram for the problem situation-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>The problem situation could be represented in the following form of a causal loop diagram<br> as shown in Figure 2.</p>
Data Workbook - Ex-Situ Geoheritage Case Study: Quantitative and Qualitative Analysis of the Uppsala University Museum of Evolution Collections
<p>Data Workbook for Thesis.</p> <p>Ex-Situ Geoheritage Case Study: Quantitative and Qualitative Analysis of the Uppsala University Museum of Evolution Collections. </p> <p>Includes; Images, Conservation Results, Inventory, Valuation Grades, RStudio Results</p>
Appendix_Results_of_quantitative_qualitative_analysis (42-language sample)
<p>A dataset showing results of quantitative and qualitative analysis on the basis of a 42-language sample.</p>
Appendix #2: Results of quntitative and qualitative analysis
<p>An unprocessed .xlsx document showing the results of quantative and qualitative analysis on the 45-language sample.</p>
Qualitative raw data and behavioral analysis for understanding VMMC policy decision-making
<p>Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia's VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0-2 months) and early-adolescent (15-17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors.</p>
Data from: A qualitative analysis of an Aβ-monomer model with inflammation processes for Alzheimer's disease
<p>We introduce and study a new model for the progression of Alzheimer's disease incorporating the interactions of Aβ-monomers, oligomers, microglial cells and interleukins with neurons through different mechanisms such as protein polymerization, inflammation processes and neural stress reactions. In order to understand the complete interactions between these elements, we study a spatially-homogeneous simplified model that allows to determine the effect of key parameters such as degradation rates in the asymptotic behavior of the system and the stability of equilibriums. We observe that inflammation appears to be a crucial factor in the initiation and progression of Alzheimer's disease through a phenomenon of hysteresis, which means that there exists a critical threshold of initial concentration of interleukins that determines if the disease persists or not in the long term. These results give perspectives on possible anti-inflammatory treatments that could be applied to mitigate the progression of Alzheimer's disease. We also present numerical simulations that allow to observe the effect of initial inflammation and concentration of monomers in our model.</p>
Inputs and results of "A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.'s case"
<p>This repository contains the datasets and visualizations generated in our work: <strong>"A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.’s case"</strong>.</p> <p><strong>Note:</strong> the data are all contained inside the <strong><em>data.zip</em> </strong>file. You need to unzip the container to get access to all the files and directories listed below.</p> <p>The data (citations) gathered accompanied by their annotated characteristics are stored in <strong><em>data/</em>:</strong></p> <ul> <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>.</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> </ul> <p><strong>Topic modeling</strong></p> <p>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 [1]. The topic modeling results for each textual feature are separated into two different folders, <em><strong>abstract/</strong></em> for the abstracts, and <em><strong>intext_cit/</strong></em> for the in-text citation contexts. Both the directories contain the datasets and visualizations generated using MITAO. </p> <p> </p> <p><strong>References</strong></p> <p>[1] 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>
A Qualitative Analysis of Themes in Instant Messaging Communication of Software Developers
<p>Data available:</p> <ul> <li>Lists of public Gitter and Slack chat rooms selected for our study (see related DOI)</li> <li>Keywords used to search Gitter chat rooms</li> <li>Name, description and analysis of 87 Gitter cha rooms (downloaded with Gitter API - https://developer.gitter.im/docs/welcome)</li> <li>Name, description and analysis of 184 Slack cha rooms (found at Slofile - https://slofile.com/)</li> </ul>
Inputs and results of "A quantitative and qualitative citation analysis to retracted articles in the humanities domain"
<p>This repository contains the datasets and visualizations generated in our work: <strong>"A quantitative and qualitative citation analysis to retracted articles in the humanities domain"</strong>.</p> <p><strong>Note:</strong> the data are all contained inside the <strong><em>data.zip</em> </strong>file. You need to unzip the container to get access to all the files and directories listed below.</p> <p>The data (citations) gathered accompanied by their annotated characteristics are stored in <strong><em>data/</em>:</strong></p> <ul> <li><em>cits.csv: </em>a dataset containing all the entities (rows in the CSV) which have cited a retracted article in the humanities domain. Each citing entity (row) is accompanied by a set of features (columns) that characterizes it.<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>.</li> <li><em>content.csv: </em>a dataset containing the abstracts and the in-text citation contexts of all the citing entities gathered.<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> <li><em>excluded_hum_retractions.csv: </em>a list of the 12 humanities retracted articles with a humanities affinity score < 2, therefore excluded from the analysis. </li> </ul> <p> </p> <p><strong>Topic modeling</strong></p> <p>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_model/</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 [1]. The directory <em><strong>workflow/ </strong></em>contains the workflows used in MITAO. The topic modeling results for each textual feature are separated into two different folders, <em><strong>abstract/</strong></em> for the abstracts, and <em><strong>cits_context/</strong></em> for the in-text citation contexts. Both the directories contain the following directories/files: </p> <ul> <li> <p><em><strong>datasets_and_views/: </strong></em>the datasets and visualizations generated using MITAO. </p> </li> <li> <p><em><strong>ldamodel_corpus_dict/: </strong></em>it contains the dictionary, the LDA topic model, and the tokenized and vectorized corpus.</p> </li> <li><em><strong>rawdata/: </strong></em>the textual collection, metadata, and stopwords used as input in the workflow of MITAO</li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] 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> </p> <ol> </ol>
Plastic additive qualitative analysis - Plastic reference materials
<p><span> Clusters of tabular data (with metadata including variable labels, code labels and definition of missing value) reporting plastic additive profile in the reference materials with ID P3, P4, P5, P6, P8 and P9. </span><span>Quality assurance measures were in place during analysis including calibration curves, blank measurements and use of validated methods.</span></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>
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.