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11,394 results for “Surveys”
A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset
<p>A list of 30+ online resources have been identified and curated by the FAIR4RS Subgroup 5: Adoption Guidelines. These resources are available as the supplementary materials of the report (<a href="https://doi.org/10.5281/zenodo.6374598">Martinez et al., 2022</a>) and can be downloaded and cited from this landing page.</p> <p><strong>The list is open for additions by the community via comments directly to this <a href="https://docs.google.com/spreadsheets/d/1pMWEyadkGW22zYaBl3LsNYoQVk9AW711-2Ogyi8UZZo/edit#gid=0">link</a>. We particularly encourgae authors of new and exisiting resources to add as much detail as possible to describe their resource and its relevance to the <a href="http://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>. Each of the columns has a description and whether the information is optional or not. Whe plan to add tags to the added resources for each semester and when there is another set of 30 resources we can resealease a new version. </strong></p> <p>This list reflects the wide spectrum of global contributions supporting the implementation of the FAIR Principles, particularly regarding research software. It is a snapshot of currently available resources, although we expect that new resources will become available in the future and that the contents of the current list will evolve. It is important to note that most of these resources precede the definition of the <a href="https://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>; however, these still support their implementation.</p> <p>The resources were manually collected, analyzed, and categorized according to their type: guidelines, tools, metadata schemas and registries/repositories. For each resource detail is also provided on which of the FAIR4RS Principles that the resource supports.</p> <p> </p> <p><strong>Data collection</strong></p> <p>This subgroup initiated a crowdsourcing effort to identify relevant resources. All members had the opportunity to provide and describe existing FAIR research software guidelines and tools. During the first two months of the subgroup operation in 2021, subgroup participants (referred to as data providers) added resources to an online spreadsheet. Data providers were encouraged to list resources that they were aware of, authored, or were supported by their institutions. Subsequently, the subgroup organized virtual calls to discuss the resources, their descriptions and the categorization. Over the next two months each data provider added descriptions to resources they were familiar with. This meant that some resources gained descriptions from different data providers. Before the completion of the list, the subgroup leads checked the list and cleaned it (removing items that lacked information or providing complementary information). The resulting list is the first crowdsourced list of its type and it welcomes your contributions!</p>
Survey data on assumed benefits of visualizations in libraries
<p>Results of a survey with 242 participants on connections between assumed benefits of visualizations in the library context and research behavior aspects (frequency of digital material usage, material focus, research subjects etc.). Additionally, participants were subjected to the visualization "An Ocean of Books" by Gaël Hugo and instructed to choose up to five adjectives out of the reaction cards subset proposed by Merčun[1].</p> <p>The head of the file contains the question ID, the question itself, and short information on the data type.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p>[1] T. Merčun. Evaluation of information visualization techniques: analysing user experience with reaction cards. In Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, BELIV ’14, pp. 103–109. Association for Computing Machinery, New York, NY, USA, Nov. 2014. doi: 10.1145/2669557.2669565</p>
Survey on Open Science Practices in Functional Neuroimaging. Dataset and Materials
<p>Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. A PubMed search with the search terms ("fMRI" OR "functional magnetic resonance imaging" OR "functional Magnetic Resonance Imaging") was done to collect email addresses from corresponding authors of scientific articles published between 2010/01/01 and 2020/08/28. An email was sent to 14,690 addresses on 2020/01/12 with an invitation to participate, including a personalized link to the survey. If the recipients did not click the link or did not complete the survey after 14 days, they received a single reminder email. The questionnaire was composed of five building blocks. The Blocks 1-3 focused on three areas of open science practices: data structure, preregistration and data sharing. The fourth block asked about technical expertise with software and the fifth part assessed sociodemographic data.</p>
Local survey network and monitoring data of VLBI telescope at Metsähovi
<p>Observations of local surveying network and monitorin at Metsähovi. More information is in file Data_description.pdf.</p>
Staff survey on awareness of gender bias in ATHENA RPOs and RFOs
<p>Task 2.3.1 from WP2 required the collection of data on awareness of gender bias to staff from the ATHENA RPOs and RFOs. An online survey was distributed among the staff of the ATHENA consortium institutions developing a gender equality plan (GEP). ATHENA institutions were requested that the sample was representative as much as possible, meaning this to consist proportionally of female/male, junior/senior positions and staff by occupations depending of the total staff composition in each institution.</p> <p>The aim of the staff survey is:</p> <ul> <li>To identify how aware are the respondents on gender equality in science and research institutions. </li> <li>To identify the biases/stereotypes related to the women´s and men´s role in science and research institutions.</li> <li>To identify gender imbalances and disadvantages in: <ul> <li>Recruitment and promotion,</li> <li>Gaining academic/scientific degree,</li> <li>Participation in decision making,</li> <li>Working conditions and workload,</li> <li>Work-life balance</li> <li>Experiences in harassment</li> </ul> </li> <li>The staff survey results will complement the results of the interviews and focus groups to provide a comprehensive picture of gender equality imbalances in the particular institution.</li> </ul> <p>The staff survey was realised through a standardised questionnaire developed by the WP2 coordinator (UVSK SAV) and approved by the Project Coordinator.</p> <p>The questionnaire consisted of 8 sections devoted to the particular gender areas- dimensions of interest:</p> <ul> <li>Introduction</li> <li>Information on the respondents ´current job</li> <li>Information of respondents´ background</li> <li>Opinions and perception of gender equality in research</li> <li>Recruitment and career development</li> <li>Striving for scientific/academic degree</li> <li>Gender balance in decision-making positions</li> <li>Workload and work-life balance</li> <li>Bulling and harassment</li> </ul> <p>Each section contained 4 – 8 closed and open questions. </p> <p>The results of the data served as support for the WP2 gender equality audit and assessment of procedures and practices at organizational level in D2.3 – Gender equality reports.</p>
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>
AMPSphere : the worldwide survey of prokaryotic antimicrobial peptides
<p><strong>AMPSphere v.2022-03: the worldwide survey of prokaryotic antimicrobial peptides</strong></p> <p> </p> <p><strong>INTRODUCTION</strong></p> <p>AMPSphere is a comprehensive catalog of antimicrobial peptides predicted using Macrel (DOI: <a href="https://peerj.com/articles/10555/">10.7717/peerj.10555</a>) from 63,410 public metagenomes, <a href="http://progenomes.embl.de/">ProGenomes v2.2 database</a> (82,400 high-quality microbial genomes), and c.a. 4k non-whitelisted microbial genomes from NCBI. Currently, AMPSphere is available as a web resource at <a href="https://ampsphere.big-data-biology.org/">https://ampsphere.big-data-biology.org/</a>.</p> <p> </p> <p><strong>GENERATION</strong></p> <p>Peptides were predicted using <a href="https://www.big-data-biology.org/software/macrel/">Macrel</a>. Singleton peptides were removed, except those with a direct hit to <a href="http://dramp.cpu-bioinfor.org/">DRAMP<br> database</a>. Redundant peptides were coded using a reduced alphabet and hierarchically clustered using CD-HIT (version 4.6) at 100%, 85%, and 75% of amino acid identity (and 90% of overlap of the shorter peptide). The obtained clusters were numbered by decreasing size (number of peptides). Each level of clustering was called a SPHERE. Redundant nucleotide sequences for the gene variants of different AMPs also were included in this version of AMPSphere.</p> <p> </p> <p><strong>STATISTICS</strong></p> <p>AMPSphere v.2022-03 contains 863,498 sequences (avg length: 36 amino acids, range 8-98). DRAMP database was used to find confirmed sequences with strict homology to reference. This approach showed that 2,488 peptides were previously confirmed in our dataset.</p> <p> </p> <p><strong>IDENTIFIERS</strong></p> <p>Peptides are named in the form <strong>>AMP10.XXX_XXX</strong> where <strong>XXX_XXX</strong> is a unique numerical identifier (starting at zero). Numbers were assigned in order of increasing number of copies. So that the lower the number, the greater number of copies of that peptide were present in the input data. Annotations were also provided as separated fields in the fasta file, containing their:</p> <p>- SPHERE families at level III (corresponding to hierarchically obtained clusters using 100-85-75% of identity with a minimum overlap of 90% of the shorter gene).</p> <p>Example of header:</p> <pre><code class="language-bash">>AMP10.000_000 | SPHERE-III.001_493 </code></pre> <p><br> <strong>VERSION DETAILS</strong></p> <p><strong>Version 2022-03</strong> includes:</p> <p>- quality assessment of documented AMPs,</p> <p>- metadata associated with the genes,</p> <p>- a better taxonomic identification of AMP sources using GTDB.</p> <p><em>WARNING: Due to a different procedure of AMP sorting, now some entries and families may have changed their accessions.</em></p> <p> </p> <p><strong>FILES WITHIN THIS VERSION</strong></p> <p><em>README.md</em><br> This file.</p> <p> </p> <p><em>AMPsphere_v.2022-03.fna.xz</em><br> Multi-fasta with AMPSphere gene sequences (nucleotide).</p> <p> </p> <p><em>AMPsphere_v.2022-03.faa.gz</em></p> <p>Multi-fasta with AMPSphere peptide sequences (amino acid).</p> <p> </p> <p><em>SPHERE_v.2022-03.levels_assessment.tsv.gz</em></p> <p>TSV table relating AMP name and the hierarchically obtained clusters per level. Columns:</p> <p>- AMP accession<br> - evaluation vs. representative<br> - SPHERE_fam level I<br> - SPHERE_fam level II<br> - SPHERE_fam level III</p> <p>Levels of each SPHERE family:</p> <p>I: contains clusters obtained with 100% of identity cut-off and 90% of overlap of the shorter sequence;</p> <p>II: contains clusters obtained with the unclustered sequences and the representatives from level I at 85% of identity and 90% of overlap of the sorter sequence;</p> <p>III: contains clusters obtained with the unclustered sequences and the representatives from level II at 75% of identity and 90% of overlap of the sorter sequence;</p> <p>`evaluation vs. representatives` shows the percent of identity the sequence has in an alignment against the cluster representative, and also the overlap in percent.</p> <p>Example:</p> <p> * -- This means: this sequence is a cluster representative.</p> <p>OR something like this:</p> <p> 77.50%,1:40:1:40 -- This means: alignment identity against the<br> representative of the cluster equals 77.5% and the<br> alignment start and end position for the query (1 and<br> 40, respectively), and target (1 and 40, respectively).</p> <p> </p> <p><em>AMPSphere_v.2022-03.quality_assessment.tsv.gz</em><br> TSV table containing the results of each quality test (by sequence). Columns:</p> <p>- AMP ID<br> - Antifam<br> - RNAcode<br> - Metaproteomes<br> - Metatranscriptomes<br> - Coordinates</p> <p>Results are one of 'Passed', 'Failed', or 'Not tested'.</p> <p><a href="https://www.ebi.ac.uk/research/bateman/software/antifam-tool-identify-spurious-proteins">Antifam </a>results show if the sequence matches ('Fail') or does not match ('Pass') to Antifams, a set of well-known spurious ORFs.</p> <p><a href="https://github.com/ViennaRNA/RNAcode">RNAcode</a> relies on gene diversity, therefore, families with less than 3 different gene sequences could not be tested and were marked as such.</p> <p>The direct match of 50% of our peptide to transcripts (in at least 2 different samples) or peptides from meta-omics studies sampled from different environments assigned the peptide as passing the metatranscriptomes and metaproteomes tests, respectively.</p> <p>Finally, the coordinates test check if the start of the small ORF happens with at least one stop codon upstream, this ensures that the gene is not a fragment from a larger protein.</p> <p> </p> <p><em>AMPSphere_v.2022-03.general_geneinfo.tsv.gz</em><br> TSV table relating AMP, gene name, the microbial source, sample, environment, and geographical location. Columns:</p> <p>- gmsc (gene code access) <br> - amp <br> - sample (biosample)<br> - source (microbial origin, GTDB taxonomy)<br> - specI (species cluster according to ProGenomes v.2 classification)<br> - is_metagenomic (False if comming from a high-quality microbial genome)<br> - geographic_location<br> - latitude<br> - longitude<br> - general_envo_name<br> - environment_material</p> <p> </p> <p><strong>CONTACT</strong></p> <p>You can contact us via our discussion group: <a href="https://groups.google.com/g/ampsphere-users">https://groups.google.com/g/ampsphere-users</a></p> <p>AMPsphere main developers:</p> <p>- <a href="mailto:celio@big-data-biology.com?subject=AMPSphere%20v.2022-03&body=Dear%20Celio%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Célio Dias Santos Júnior</a><br> - <a href="mailto:yiqian@big-data-biology.org?subject=AMPSphere%20v2022-03&body=Dear%20Yiqian%2C%20%0A%0ARegarding%20AMPSphere%20v2022-03.%0A">Yiqian Duan</a><br> - <a href="mailto:hui@big-data-biology.org?subject=AMPSphere%20v2022-03&body=Dear%20Hui%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Hui Chong</a><br> - <a href="mailto:luispedro@big-data-biology.com?subject=AMPSphere%20v.2022-03&body=Dear%20Luis%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Luis Pedro Coelho</a></p> <p><br> <strong>COPYRIGHT NOTICE</strong></p> <p><em>AMPSphere v.2022-03 - the worldwide survey of prokaryotic antimicrobial peptides.</em></p> <p>This work is a joint effort of Big Data Biology group from the Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) - Fudan University, Shanghai, China, and the Structural and Computational Biology Unit<br> (Heidelberg) - European Molecular Biology Laboratory (EMBL).</p> <p>Copyright (C) 2019-2022 The Authors</p> <p> AMPSphere IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES<br> OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,<br> DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR<br> OTHERWISE,ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE<br> USE OR OTHER DEALINGS IN THE SOFTWARE.</p> <p> This database is free; you can redistribute it and/or modify it<br> as you wish, under the terms of the CC BY 4.0 license.</p> <p> You are allowed to:</p> <p> Share — copy and redistribute the material in any medium or format</p> <p> Adapt — remix, transform, and build upon the material for any purpose,<br> even commercially.</p> <p> You may also obtain a copy of the CC BY 4.0 license here:<br> <br> https://creativecommons.org/licenses/by/4.0/</p> <p><br> <strong>REFERENCES CITED</strong></p> <p>- Macrel: Santos-Júnior CD, Pan S, Zhao X, Coelho LP. 2020. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 8:e10555. https://doi.org/10.7717/peerj.10555</p> <p>- ProGenomes: Mende DR, Letunic I, Maistrenko OM et al. 2020. proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes. Nucleic Acids Research 48(D1): D621–D625. https://doi.org/10.1093/nar/gkz1002</p> <p>- DRAMP: Kang X, Dong F, Shi C et al. 2019. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data 6, 148. https://doi.org/10.1038/s41597-019-0154-y</p> <p>- ANTIFAM: Eberhardt RY, Haft DH, Punta M, Martin M, O’Donovan C, BatemanA. 2012. AntiFam: a tool to help identify spurious ORFs in protein annotation. Database, Bas003.</p> <p>- RNAcode: Washietl S, Findeiss S, Müller SA, Kalkhof S, von Bergen M, Hofacker IL, Stadler PF, Goldman N. 2011. RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17(4):578-94.<br> </p>
Online survey of needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector
<p>The present dataset was generated as part of the "Needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector" of the H2020 <a href="http://innorbit.eu">InnORBIT project</a>.</p> <p>The aim of this study was to identify and explore the available and missing skills of innovation intermediaries to provide business support services to innovators within their local ecosystems to develop commercial activity in space. The assessment of skills was based on a baseline framework encompassing a wide array of skills and competencies innovation intermediaries are supposed to possess in order to provide effective business support services to space innovators. The skills of the baseline framework are grouped into five broad categories: (i) space industry knowledge, (ii) business assessment knowledge, (iii) business support skills, (iv) organisational and digital skills and (v) soft skills. The baseline framework was originally developed by the InnORBIT consortium through research in related works of EU funded projects and publications and validated through a series of 15 interviews with top-level executives of organisations across the CEE and SEE area, belonging to the two target groups of the study (i.e., innovation intermediaries and innovators). An online survey was deployed from May 26th to June 18th using the EU Survey tool, to innovation intermediaries and innovators across the EU and CEE/SEE countries in particular. Two online questionnaires were developed building on the baseline skills framework - the first intended for innovation intermediaries asking them to perform a self-assessment of their skills in terms of providing business support services and the second targeting innovators, asking them to state their perception on how innovation intermediaries they have worked with, perform in each of the skills.</p> <p>The dataset contains four files:</p> <p>1. Zip file including the transcripts from 6 interviews with space innovators in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>2. Zip file including the transcripts from 9 interviews with innovation intermediaries in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>3. a pdf file of the digital questionnaires developed in EU Survey deployed to innovation intermediaries and innovators in the region</p> <p>4. An excel file with 104 valid responses collected from the online survey (56 innovation intermediaries and 48 innovators) across 16 EU countries / 21 countries total.</p> <p>The dataset contains only non-sensitive anonymised information and is in full compliance with the GDPR provisions. Any information leading to the identification of participants in activities (interviews, survey) is either modified or omitted and deonted with brackets.</p>
Survey Base Calculation tool. Raw data.
<p>Raw data from survey application of Perceptions towards the adoption of Bio-based fertilisers. </p>
COST-ARKWORK Societal Challenges Survey Dataset
<p>A survey was relating to the societal role of archaeology and the impact of societal challenges on archaeological practices is based on views collected from the members of COST Action Archaeological Practices and Knowledge Work in the Digital Environment (<a href="http://www.arkwork.eu/">www.arkwork.eu</a>). The purpose of this survey is to collect views from COST-ARKWORK members on the relation of contemporary societal challenges and archaeological practices. The survey was administered online using Survey & Report tool (survey form, Fig. 1) hosted by Swedish University Computer Network (Sunet). The survey was open from May 8, 2020 until June1, 2020. </p> <p> </p> <p>The participants of the network consist of over 200 experts of archaeological practices from a broad range of disciplinary backgrounds from archaeology to information science, museum studies, computer science and business studies, including researchers and practitioners from 30 European countries. 50 members of the network participated in the survey. The views of the Action participants were collected using an online survey with two open ended questions: 1. “From your perspective, what is the role and value of archaeology in helping to solve contemporary societal problems?”; and, 2. “What societal changes and challenges are likely to affect archaeology and archaeological practices the most during the next five years?”. In addition, the respondents were asked to indicate whether they identified themselves as being an archaeologist or not. 64% (32/50) of the experts identified themselves as archaeologists while the rest described themselves as non-archaeologists. The experts shared their opinions as individuals, not as representants of specific institutions or countries.</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>
Basic data visualisations for Figshare State of Open Data 2021 survey
<p>R markdown files for:</p> <ul> <li>Downloading and cleaning data from the State of Open Data survey 2021</li> <li>Basic visualisations of responses to questions in the State of Open Data survey 2021</li> <li>HTML file of those visualisations.</li> </ul> <p>Free text fields are included in the markdown but have been turned off for knitting and in the HTML file.</p>
Project Information Model resulting from the on-site survey
<p>The On-site Analysis and Verification Service (ODAVS) is a service developed within the Encore project. It allows users to check any constructability issues regarding renovation projects of residential buildings by means of surveys facilitated by a mixed reality tool. </p> <p>This dataset includes two IFC files of the renovation studies developed by Univpm and JEA (both partners of Encore project) at JEA experimental building in Caceres, and assessed on-site on 2021 December 14th and 15th. The dataset also includes an XML file containing the list of 33 URLS pointing to audio files previously published on a different Zenodo dataset [1]. Note that the access to the Zenodo dataset [1] is restricted. The interested users must ask the dataset authors for permission to access the dataset itself. In the XML file, for each comment, the GUID of the IFC object referred by the audio comment itself is given.</p> <p>References</p> <p>[1] "Pictures and Videos Collected During ODAVS Activity", by Carbonari A. and Vaccarini M., DOI 10.5281/zenodo.6531860, URL: https://doi.org/10.5281/zenodo.6531860</p> <p> </p>
Data products for the XMM-SERVS survey
<p>These are the XMM-Newton data products from the XMM-SERVS survey associated with the Ni et al. (2021) paper: "The XMM-SERVS survey: XMM-Newton point-source catalogs for the W-CDF-S and ELAIS-S1 fields", ApJS, 256, 21, and the Chen et al. (2018) paper: "The XMM-SERVS survey: New XMM-Newton point-source catalogue for the XMM-LSS field”, MNRAS, 478, 2132.</p>
Orangutan habitat survey in Sebangau National Park, Central Kalimantan, Indonesia
<p>This dataset is used to initialise BORNEO (arBOReal aNimal movEment mOdel), as a part of publication entitled:</p> <p>Assessing the impact of forest structure disturbances on the arboreal movement oforangutans - an agent-based modelling approach.</p> <p>The article manuscript is being prepared to be submitted to Frontiers in Ecology and Evolution</p> <p><strong>Data collection</strong></p> <p>The data is collected in Sebangau, Central Kalimantan, Indonesia. Two 1-ha plots were established, each in unburned and burned forest. </p>
Improving students' privacy awareness – Analysis of a pilot survey to design a VR environment for self-paced learning
<p>In this research, we measured the knowledge of students at the University of Debrecen in the field of data privacy awareness, online and password security.</p> <p><strong>Description</strong></p> <ul> <li>In the questionnaire, green-highlighted answer signs the correct answer to each question.</li> <li>Total data set contains the answers to each question and the respondent's age.</li> <li>Correct/incorrect data set contains information if the answer is correct to each question, and it also contains the respondent's age. One means the answer was correct, and zero means the answer was incorrect.</li> </ul>
Dataset: Multi-level network dataset of social-ecological interdependencies in ten Swiss wetlands based on qualitative interviews and quantitative surveys
<p>The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).</p>
Valencia Archaeological Surveys (1990-2004), Arizona State University and University of Valencia
<p>Archaeological survey data from Valencia, Spain</p>
Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID- 19 CDMX – JULY 2020)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID-19 related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the first dataset of the project, corresponding to July 2020, collected four months after the lockdown began in Mexico. Data collection was performed between the 8th and the 17th of July.</p>
Survey on lattice data analysis, presentation, and curation practices
<p>This repository contains the results of a survey on software workflows and open science in lattice field theory conducted in 2022 by Andreas Athenodorou, Ed Bennett, Julian Lenz, and Elli Papadopolou. These data were collected using <a href="https://www.limesurvey.org/">LimeSurvey</a>, and were first presented in <a href="https://indico.hiskp.uni-bonn.de/event/40/contributions/695/">a talk at Lattice 2022 by Andreas Athenodorou</a>.</p> <p>The analysis is based on Julian Lenz's <a href="https://github.com/chillenzer/limesurvey-parser">LimeSurvey CSV parser</a>.</p> <p>The survey results are included in survey-results-redacted.csv. The survey structure is included in survey-structure.lss. Further details of the structure of the data, setup, see the included README.md file.</p>
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.