Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project

"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.

opencc-by-nc-sa-4.0Dec 2021View details →

SSHOC - National Gallery - Raphael Research Resource CIDOC CRM Mapped Dataset

In 2007 the <a href="https://cima.ng-london.org.uk/documentation">Raphael Research Resource</a> project began to examine how complex conservation, scientific and art historical research could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images and text, along with how the data could be stored in relation to an event driven ontology in the form of <a href="http://www.w3.org/TR/rdf-concepts/">RDF triples</a>. The original <a href="https://cima.ng-london.org.uk/documentation">main user interface</a> is still live, In 2021/21 as part of the <a href="https://www.sshopencloud.eu/">SSHOC Project</a> the raw data stored within the system was mapped to the <a href="https://www.cidoc-crm.org/">CIDOC CRM</a> using a custom set of Python scripts (<a href="https://doi.org/10.5281/zenodo.6461654">https://doi.org/10.5281/zenodo.6461654</a>). The SSHOC work aimed to make this data more <a href="https://www.go-fair.org/fair-principles/">FAIR</a> so in addition to mapping it to a standard ontology, to increase Interoperability, it has also been made available in the form of <a href="http://en.wikipedia.org/wiki/Linked_Data">open linkable data</a> combined with a <a href="http://en.wikipedia.org/wiki/SPARQL">SPARQL</a> end-point. This live data presentation can been found <a href="https://rdf.ng-london.org.uk/sshoc/">Here</a>. This deposit contains the CIDOC-CRM mapped data formatted in XML and an example model diagram representing some of the key relationships covered in the data-set.

opencc-by-4.0Jul 2022View details →

References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)

The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).

opencc-by-4.0Nov 2017View details →

Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software

Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.

opencc-zeroNov 2019View details →

NOAA NCCOS Assessment: Prioritizing Areas for Future Seafloor Mapping, Research, and Exploration Offshore of California, Oregon, and Washington from 2019-03-01 to 2019-04-01

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington.  This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals. There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa et al. 2019.

opencc-zeroJun 2023View details →

A mapping of keywords from published papers on alien squirrels to biological invasion research themes

Context This dataset was used to produce the worldl and the graphs in the editorial to the research topic <a href="https://www.frontiersin.org/research-topics/29270/ecology-impact-and-management-of-squirrel-invasions">Ecology, impact and management of squirrel invasions</a> (La Morgia et al. 2023). Contents of the dataset The dataset contains the keywords of papers since 2000 harvested with a Web of Science search (performed on 29/05/2023) using the advanced search string TS=(invasive squirrel) OR TI=(invasive squirrel) OR AB=(invasive squirrel). We screened the search results, excluding papers irrelevant to alien squirrels, for example, papers on computer science or physiology, medical or other aspects without any bearing to conservation science. To do this, we checked the abstract and keywords of the papers. Out of the 401 initial papers, after this first screening, we kept 217 in this dataset. The keywords of these papers were manually assigned to alien squirrel research topics by the authors of this dataset (using an own categorisation) and then mapped to the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a>:  <ol> <li>Ecosystems: topics which discuss a specific region, or biome, or focused on a particular species strongly associated with one ecosystem type;</li> <li>Monitoring: topics regarding all aspects of monitoring, including detection, identification, and distributional mapping;</li> <li>Management and decision-making: topics discussing the management and socio-political aspects of invasion science, such as prevention, control, and policy;</li> <li>Interactions: topics discussing the interactions with native species, or the effects of those interactions</li> <li>Assessing change: topics focused on studying and analysing temporal and ecological change;</li> <li>Traits: topics that explored the characteristics of alien squirrels;</li> <li>Invasion mechanisms: topics discussing dispersal pathways and drivers of spread.</li> </ol> Dataset description Every row (N = 1275) in the comma-separated .csv represents one original keyword with reference to the paper in which that keyword appears and mapped to the research topics on invasive squirrels and the broad themes in invasion biology research. The .csv contains the following fields: <ul> <li>ID: a unique ID assigned to the combination of an original keyword and the corresponding paper harvested from the WoS search</li> <li>original_keyword: the original keywords associated with the paper (WoS search)</li> <li>keyword_topic: categorization of original keywords into topics related to invasive squirrel research by La Morgia et al. (2023)</li> <li>mapped_category: mapping to one of the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a> as listed and described above</li> <li>authors: author(s) of the paper (WoS search)</li> <li>year: publication year of paper (WoS search)</li> <li>title: title of the paper (WoS search)</li> <li>journal: full journal name (WoS search)</li> <li>doi: full doi of the paper (WoS search)</li> </ul> Potential applications of the dataset This dataset can be used to reproduce the graphs in La Morgia et al. (2023) or to perform more in-depth review or analysis of the literature on alien squirrel invasions. For more information and graph code, we refer to <a href="https://github.com/Vale-LaMo/squirrels">this GitHub repository</a>.

edi44/100

Map of ecological sites and ecological states for pastures 1, 4, 14, and 15 on the Chihuahuan Desert Rangeland Research Center, New Mexico

This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.

openCC (other)Feb 2023View details →

opencc-by-4.0May 2020View details →

Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs). <a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as proof of practice.) In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America. 17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs. The survey contains the following questions (see inside dataset for exact wording): <ul> <li>Are you familiar with this SDG? <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li>Suggest research papers that are relevant for this SDG (upload list) <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li>Select research papers that are relevant for this SDG (radio buttons: accept, reject) <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions) <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Suggest SDG related glossaries with relevant keywords (text fields: url) <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions) <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Suggest improvements for the current queries (text field: suggestions per target) <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> In the dataset root you'll find the following folders and files: <ul> <li>/00-survey-input/ <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li>/01-raw-data/ <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li>/02-aggregated-data/ <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li>/03-scripts/ <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li>/04-processed-data/ <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</li> </ul> </li> <li>/images/ <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li>LICENSE.md <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li>README.md <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> In the /04-processed-data/ you'll find in each SDG sub-folder the following files.: <ul> <li>SDG-survey-questions.pdf <ul> <li>This file contains the survey questions</li> </ul> </li> <li>SDG-survey-questions.doc <ul> <li>This file contains the survey questions</li> </ul> </li> <li>SDG-survey-respondents-per-sdg.csv <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li>SDG-survey-city-heatmap.csv <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li>SDG-survey-suggested-publications.txt <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li>SDG-survey-suggested-publications-with-eid-match.csv <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li>SDG-survey-selected-publications-accepted.csv <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li>SDG-survey-selected-publications-rejected.csv <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li>SDG-survey-selected-keywords.csv <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li>SDG-survey-unselected-keywords.csv <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li>SDG-survey-suggested-keywords.csv <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-glossaries.csv <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-selected-journals.csv <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li>SDG-survey-unselected-journals.csv <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li>SDG-survey-suggested-journals.csv <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-suggested-query.csv <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> Cite as: <blockquote> Survey data of "Mapping Research output to the SDGs" by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a> </blockquote> Attribute as: <blockquote> Survey data of "Mapping Research output to the SDGs" by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA); is licensed under a Creative Commons Attribution 4.0 International License. <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a> </blockquote>

opencc-by-sa-4.0Oct 2023View details →

Package and Dependency Metadata for CZI Hackathon: Mapping the Impact of Research Software in Science

A collection of useful datasets extracted from <a href="https://packages.ecosyste.ms">https://packages.ecosyste.ms</a> and <a href="https://repos.ecosyste.ms/">https://repos.ecosyste.ms</a> for use at the CZI Hackathon: Mapping the Impact of Research Software in Science.All data is provided as NDJSON (new line delimited JSON), each line represents a valid JSON object, and they are separated by newline characters. There are <a href="https://pypi.org/project/ndjson/">python</a> and <a href="https://www.rdocumentation.org/packages/ndjson/versions/0.9.0/topics/stream_in">R</a> libraries for reading these files, or you can maually read each line and parse each line as a single JSON object.Each ndjson file has been compressed with gzip (actual command: `tar -czvf`) to reduce download size, they expand to significantly bigger files after extraction.<h4>Package Data</h4>Package names from cran, bioconductor and pypi that have been parsed by the <a href="https://github.com/chanzuckerberg/software-mentions">software-mentions</a> project (data: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c">https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c</a>) are collected together with their latest release at time of publishing along with the names of their dependencies, those dependency names have then also been recursively fetched with latest release and dependencies until the full list of transitive dependencies is included. Note: This approach uses a simplified method of dependency resolution, always picking the latest version of each package rather than taking into account each dependencies specific version range requirements, this is primarily due to time constraints and allows all software ecosystems to be processed in the same way. A future improvement would be to use each package ecosystem's specific dependency resolution algorithm to compute the full transitive dependency tree for each mentioned software package.<h4>GitHub Data</h4>Two different approaches were taken for collecting data for referenced GitHub mentions:1. `github.ndjson` is metadata for each repository from GitHub, including "manifest" files which are known files that contain dependency information for a project such as requirements.txt, DESCRIPTION and package.json, parsed using <a href="https://github.com/ecosyste-ms/bibliothecary">https://github.com/ecosyste-ms/bibliothecary</a>, which may include transitive dependencies that have been discovered in a `lockfile` within the repository.2. `github_packages.ndjson` is metadata for each package that was found on any package manager that references the GitHub url as it's repository url/source/homepage, these packages, like the cran and pypi data above, include the latest release and their direct dependencies. There may be more than one package for each GitHub URL as it is a one to many relationship. `github_packages_with_transitive.ndjson` follows the same format but also includes the extra resolved transitive dependencies of all packages using the same approach as with cran and pypi data above with the same caveats. There are also many more ecosystems referenced in these files than just cran, bioconductor and pypi, https://packages.ecosyste.ms provides a standardized metadata format for all of them to enable comparison and simplification of automation.<h4>Contact</h4>If you would like any help, support or more data from Ecosyste.ms please do get in touch via email: hello@ecosyste.ms or open an issue on GitHub: https://github.com/ecosyste-ms/packages/issues

opencc-by-4.0Sep 2024View details →

Dataset for ´´A New Detailed Global Map of Lunar Light Plains´´ research article

The shapefiles (.shp) provided in this repository are the datasets for the paper ´A new detailed global map of lunar light plains´ published in PSJ journal Special Issue.  These shapefiles can be directly imported in ArcMap/ArcPRO. The third dataset is a .tif or image of the global map for a fast and easy overview. Two geomorphologic maps of lunar light plains are provided as described in the article: one with an FeO wt% cut off of about 12 wt% (Area_lightplains), and the other around 8 wt% (Area_LPFeOLow).

opencc-by-4.0Sep 2021View details →

Replication Data for "Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades"

In this research (publication included in the package), we have used author-assigned keywords as a quantitative data source for understanding the connections between keywords and research topics in software testing research, based on a large sample of studies from Scopus. We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. This package contains the map and network files used to perform our analyses, as well as the publication sample.

opencc-by-4.0Sep 2022View details →

Story Map of the Technical Research Centre of Finland (VTT)

The Co-Change Lab at the Technical Research Centre of Finland (VTT) focuses on co-designing and implementing VTT’s sustainability programme. This work is a collaborative process that involves VTT’s sustainability responsibility researchers and the nominated responsibility task force.

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics, approaches and contributions of research about CRIS

The size of the diamonds indicates the amount of articles belonging to each category. This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study

This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics covered in the analyzed articles

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Process of systematic mapping

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications per country

Publications per country. Map based on longitude and latitude. The circle’s colors show each country, while their size indicates the number of articles. This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.