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235 results for “survey research”
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries
<p>This document provides extended, supplementary data and information to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- The raw dataset of survey entries, anonymized (IP addresses and personal comments deleted)</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Survey responses
<p>The spreadsheets in the present dataset (CSV format) include the anonymised responses to our online survey of signatories of the Joint Statement on open research and data sharing. Responses have been split into quantitative responses (i.e., closed survey questions) and qualitative responses (i.e., free text survey questions).</p> <p>This data has been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo Project Community</a>.</p>
Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis
<p>Dataset of the research paper: <strong>Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis</strong></p> <p>Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this and investigate the outcomes of impactful research, we performed a quantitative and qualitative analysis of 4 354 SE patents citing 1 690 SE papers published in four leading SE venues between 1975–2017. Moreover, we conducted a survey on 475 authors of 593 top-cited and awarded publications, achieving 26% response rate. Overall, researchers have equipped practitioners with various tools, processes, and methods, and improved many existing products. SE practice values knowledge-seeking research and is impacted by diverse cross-disciplinary SE areas. Practitioner-oriented publication venues appear more impactful than researcher-oriented ones, while industry-related tracks in conferences could enhance their impact. Some research works did not reach a wide footprint due to limited funding resources or unfavorable cost-benefit trade-off of the proposed solutions. The need for higher SE research funding could be corroborated through a dedicated empirical study. In general, the assessment of impact is subject to its definition. Therefore, academia and industry could jointly agree on a formal description to set a common ground for subsequent research on the topic.</p> <p>The following data files are included.</p> <ul> <li><em>./fields</em>: <ul> <li><strong>engi-fields.csv</strong>: Publication and PhD dissertation counts of main engineering branches</li> <li><strong>engi-fields-queries.txt</strong>: Queries applied to Elsevier's Scopus and Open Access Theses and Dissertations databases to retrieve the publication and dissertation counts</li> </ul> </li> <li><em>./patents</em>: <ul> <li><strong>sample-se-references-verified.csv</strong>: Manual verification of a random sample of references by software engineering (SE) patents to SE papers</li> <li><strong>se-cpc.tsv</strong>: Manually-identified SE-related Cooperative Patent Classification (CPC) categories</li> <li><strong>se-references-in-patents.csv</strong>: SE references made by SE patents to SE papers</li> <li><em>./patents/litigation</em>: <ul> <li><strong>case-values.csv</strong>: Manually-retrieved litigation damages of citing SE patents</li> <li><strong>lit-per-paper.csv</strong>: Litigation cases of citing SE patents</li> </ul> </li> <li><em>./patents/maintenance</em>: <ul> <li><strong>maint-code-fee-mapping.csv</strong>: Mapping of patent maintenance fee codes to their fee values</li> <li><strong>maint-fees.csv</strong>: Fee values of maintenance fee codes</li> <li><strong>maint-per-paper.csv</strong>: Maintenance fee events of citing SE patents</li> </ul> </li> <li><em>./patents/reports</em>: <ul> <li><strong>lit-sum-per-paper.csv</strong>: Counts and total damages of litigation cases of patent-cited SE papers</li> <li><strong>maint-sum-per-paper.csv</strong>: Counts and total values of maintenance fee events of patent-cited SE papers</li> <li><strong>patent-ref-counts.csv</strong>: SE patent citation counts of patent-cited SE papers</li> </ul> </li> </ul> </li> <li><em>./survey</em>: <ul> <li><strong>emse-top.csv</strong>: Most-cited papers of the Empirical Software Engineering (EMSE) journal</li> <li><strong>icse-bp.csv</strong>: Distinguished papers of the International Conference of Software Engineering (ICSE)</li> <li><strong>icse-mip.csv</strong>: Most influential ICSE papers</li> <li><strong>icse-top.csv</strong>: Most-cited ICSE papers</li> <li><strong>survey-questionnaire-emse.pdf</strong>: The EMSE survey questionnaire</li> <li><strong>survey-questionnaire.pdf</strong>: The ICSE, TSE, and TOSEM survey questionnaire</li> <li><strong>survey-responses.csv</strong>: The anonymized survey responses</li> <li><strong>tosem-top.csv</strong>: Most-cited papers of the ACM Transactions on Software Engineering and Methodology (TOSEM)</li> <li><strong>tse-top.csv</strong>: Most-cited papers of the IEEE Transactions on Software Engineering (TSE)</li> <li><em>./survey/manual-coding</em>: <ul> <li><strong>feedback.txt</strong>: Manual coding of survey feedback</li> <li><strong>practical-impact.csv</strong>: Manual coding of responses about practical impact of work</li> <li><strong>practical-impact-lack.csv</strong>: Manual coding of responses about lack of practical impact</li> <li><strong>research-methods.csv</strong>: Manual coding of additional research methods of surveyed papers</li> <li><strong>state-of-practice.csv</strong>: Manual coding of responses about changes in state of practice</li> </ul> </li> </ul> </li> <li><em>./venues</em>: <ul> <li><strong>se-venues.csv</strong>: Top SE venues according to Google Scholar Metrics</li> <li><strong>se-venues-impact.csv</strong>: SE patent citations and patent-based impact factors of SE venues</li> <li><strong>se-venues-scopus-queries.txt</strong>: Queries applied to Scopus to retrieve the publication counts of the SE venues</li> </ul> </li> </ul>
Grand Bay National Estuarine Research Reserve Seagrass Survey Data (2005 - 2010)
<p>Seagrass beds at the Grand Bay National Estuarine Research Reserve were surveyed using a transect method, twice a year at five sites.</p> <p>Details of the transect location and methods are described in the following:</p> <p>https://www.jstor.org/stable/26367667</p>
Research data management in the German-speaking Sports Sciences - Survey on the Status Quo
<p>The data set contains survey data on the status quo of research data management within the German-speaking sports science community. The survey was conducted as an online survey in the period from August 16<sup>th</sup> to September 30<sup>th</sup>, 2023.</p>
GEDII Survey on Research Teams Dataset
<p>This dataset contains the result of the GEDII cross-country survey carried out among EU based research teams during the year 2017. It contains research teams from Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Italy, Lithuania, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland and the UK. Overall, the Consortium recruited 159 research groups submitting a total of 1501 online questionnaires - out of which 1357 are complete.</p> <p>The dataset <strong>is unique</strong> in that it combines individual level responses from research group members regarding team climate, communication patterns, or leadership style among others with bibliometric performance data for each particular group retrieved from Web of Science (Clarivate Analytics).</p> <p>The data is distributed as R package "gediiccs", including extensive documentation.</p> <p>NOTE: The open access version of the "gediiccs" package contains the <strong>team level data only</strong>. This restriction is due to confidentiality concerns: given the large quantity of variables in relation to the relatively small research groups, it would be easy for group members to "reverse engineer" the identity of their own group as well as the responses from individual group members. Hence, only<strong> team level aggregated data</strong> has been released.</p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions
<p><strong>Looking into the Futures: Delphi Survey about the MENA region</strong></p> <p>In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled.</p> <p>The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).</p>
Survey for coordinators of agroecology research projects
<p>This dataset (Project_coordinators_survey.csv) contains data related to the survey launched within the Task 1.3 of AE4EU project for the coordinators of agroecology research projects funded by European, transnational, and national programmes identified in the mapping activities. Together with the dataset, the structure of the questionnaire related to this survey is also provided (Project_coordinators_questionnaire_structure.pdf).</p> <p>Answers from respondents were anonymised before the publication. Informed consent was obtained from participants to the survey to use their answers and quotations for research and publication</p>
Survey for researchers involved in Agroecology
<p>This dataset (Agroecology_Researchers_survey.csv) contains data related to the survey launched within the Task 1.3 of AE4EU project for the researchers involved in agroecology. Together with the dataset, the structure of the questionnaire related to this survey is also provided (Agroecology_Researchers_questionnaire_structure.pdf). Answers from respondents were anonymised before the publication. Informed consent was obtained from participants to the survey to use their answers and quotations for research and publication</p>
Ethics of Research Funding: A survey
<p>Anonymized data set of the ethics of research funding survey. To anonymize the data, the responses to the open comment field (Q57) have been removed. The publication of these anonymized data has been approved by the ethics committee of KU Leuven.</p>
ETHNA Validation Survey on Drivers and Barriers of Responsible Research & Innovation (RRI)
<p>This entry includes the ETHNA Validation Survey on Drivers and Barriers of Responsible Research & Innovation (RRI) questions in PDF and the servey results in Excel. Ths survey was conducted by Centre for Social Innovation (ZSI) as part of the WP4 of the European ETHNA System project. The survey explores potential good RRI practices and possible measures to gauge the RRI institutionalisation progress at research-performing organisations.</p> <p>The online survey was sent in October 2021 to a broad group (10.000+) of potentially relevant expert stakeholders identified through a Web of Science database search with the aim of assessing the relevance of the identified drivers, barriers and good practices of RRI institutionalisation. Altogether 888 responses were received from 69 countries with a balanced gender representation, involving the opinion of mostly senior experts (55% having more than 15 years of experience). After filling out general demographic and organisational information, the respondents rated the perceived relevance of the RRI incentives, barriers and good practices that were the highest ranked at the end of the second consultation phase (using a Likert scale of 1-10). </p>
CaRCC Research Computing and Data (RCD) Workforce Survey Data 2021 - Part 2
<p>Data sets to accompany "Compensation of Academic Research Computing and Data Professionals"</p> <p>Paper citation: Christina Maimone, Carrie Brown, Kimberly Grasch, Chris Reidy, and Ashley Stauffer. 2023. Compensation of Academic Research Computing and Data Professionals. In Proceedings of Practice and Experience in Advanced Research Computing (PEARC23). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3569951.3593599</p> <p>Survey questions: Maimone, Christina, Yockel, Scott, Middelkoop, Timothy, Alameda, Jay, Stauffer, Ashley, & Neeser, Amy. (2021). CaRCC Research Computing and Data (RCD) Workforce 2021 Census Questions (1.0). Zenodo. https://doi.org/10.5281/zenodo.5914431</p>
Data for Research Assessment in the Transition to Open Science. 2019 EUA Open Science and Access Survey Results
<p>This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html">https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available:</p> <ul> <li>2019 EUA Open Science and Access Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel)</li> <li>Survey Codebook: includes information on all the variables and their coding.</li> </ul>
Dataset: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.
<p>Dataset for: Koschollek C, Kuehne A, Müllerschön J, Amoah S, Batemona-Abeke H, Dela Bursi T, Mayamba P, Thorlie A, Mputu Tshibadi C, Wangare Greiner V, Bremer V, Santos-Hövener C: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.</p> <p>This dataset has been described in a PLoS One paper and contains all data necessary to replicate the results presented within this paper (10.1371/journal.pone.0227178). Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>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).</strong></p> <p><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.)</p> <p>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<strong> capture expert knowledge from senior researchers in their research domain related to the SDG</strong>. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. <strong>The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.</strong></p> <p>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.<strong> 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</strong>.</p> <p><strong>The survey contains the following questions (see inside dataset for exact wording):</strong></p> <ul> <li><strong>Are you familiar with this SDG?</strong> <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><strong>Suggest research papers that are relevant for this SDG (upload list)</strong> <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><strong>Select research papers that are relevant for this SDG (radio buttons: accept, reject)</strong> <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><strong>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)</strong> <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><strong>Suggest SDG related glossaries with relevant keywords (text fields: url)</strong> <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><strong>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)</strong> <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><strong>Suggest improvements for the current queries (text field: suggestions per target)</strong> <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> <p><strong>In the dataset root you'll find the following folders and files:</strong></p> <ul> <li><strong>/00-survey-input/</strong> <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><strong>/01-raw-data/</strong> <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><strong>/02-aggregated-data/</strong> <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><strong>/03-scripts/</strong> <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li><strong>/04-processed-data/</strong> <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><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <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> <p><strong>In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:</strong></p> <ul> <li><strong>SDG-survey-questions.pdf</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-questions.doc</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-respondents-per-sdg.csv</strong> <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li><strong>SDG-survey-city-heatmap.csv</strong> <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li><strong>SDG-survey-suggested-publications.txt</strong> <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><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong> <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><strong>SDG-survey-selected-publications-accepted.csv</strong> <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><strong>SDG-survey-selected-publications-rejected.csv</strong> <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><strong>SDG-survey-selected-keywords.csv</strong> <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><strong>SDG-survey-unselected-keywords.csv</strong> <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-keywords.csv</strong> <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-glossaries.csv</strong> <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-selected-journals.csv</strong> <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><strong>SDG-survey-unselected-journals.csv</strong> <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-journals.csv</strong> <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-suggested-query.csv</strong> <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> <p><strong>Cite as:</strong></p> <blockquote> <p><em>Survey data of "Mapping Research output to the SDGs"</em> by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a></p> </blockquote> <p><strong>Attribute as:</strong></p> <blockquote> <p><em><strong>Survey data of "Mapping Research output to the SDGs</strong>"</em> 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);<strong> is licensed under a Creative Commons Attribution 4.0 International License.</strong> <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a></p> </blockquote>
FIG. 9 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 9. — Sample size and sorting process according to the sampling type and impact on the quantity and availability of specimens for taxonomic study. Process for broad-spectrum traps: A, automatic light trap with blue LED; B, yield of the trap after one week; C, conditioning of the sample in the field laboratory, and storage in WhirlPack bags with alcohol; D-F, sorting specimens by order and family at the SEAG laboratory (Montjoly, French Guiana); G, preparing packages with glassine envelopes and Eppendorf vials for dissemination among coordinators and/or taxonomic experts; H, typical output of this kind of broad-spectrum trap samples: about 50% fraction may finally be studied (arbitrary estimate). Photos: Julien Touroult.
FIG. 10 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 10. — Process for active collecting: A, active collection of cricket (Orthoptera); B, photography of live specimens, important part of the process in some groups; C, preparation and management of the specimens for short term storage in the field laboratory; D, output of the active or selective methods: lower yields than broad-spectrum traps but a larger proportion is effectively studied. Photos: A, C, Xavier Desmier; B, Julien Touroult.
FIG. 12 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 12. —Database and portals for entry, consultation and access to data, illustrated by means of a newly described Mitaraka species, Eupromera pascali Dalens, 2016 (Coleoptera, Cerambycidae): A, CardObs database entry interface (https://cardobs.mnhn.fr). The morphospecies name was initially entered as "Eupromera n. sp." in April 2015 and after publication (Feb. 2016), the morphospecies name was replaced by the species name, and the record was completed with publication reference and the collection deposit number; B, INPN French Natural Heritage consultation portal, displaying this species from the Mitaraka dataset (https://inpn.mnhn.fr/espece/cd_nom/814643/tab/rep/GUF); C, public interface to database of the Coleoptera collection (EC) of the MNHN illustrating the holotype and its labels, with full traceability (http://coldb.mnhn.fr/catalognumber/mnhn/ ec/ec7591); D, International GBIF Data Portal displaying the Coleoptera collection (EC) dataset of the MNHN (https://www.gbif.org/occurrence/1413051340).
FIG. 8 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 8. — Active and passive substrate sampling: A, B, collection of dead branches infested with saproxylic larvae for "rearing" in emergence chambers (EXL); C, sampling soil litter for invertebrates with Winkler sieve (WS); D, spraying trunks with insecticide to collect small bark-dwelling arthropods that fall on the white sheet at the bottom of the trunk; E, searching for Annelida in soil samples collected with a spade; F, fish sampling in a small stream using rotenone. Photos: A, B, Stéphane Brûlé; C, Benoît Fontaine; D, Jürgen Schmidl; E, F, Xavier Desmier.
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.