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191 results for “open research”
Survey on how do Brazilian Software Engineering Researchers Perceive and Practice Open Science
<p>This document provides our survey questionnaire and the responses of 31 researchers.</p>
Dataset relating the Social activity of Open Research Data on ResearchGate
<p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. Although current research has investigated the way ORD is being published, researchers’ behaviour of ORD publishing and sharing on academic social networks (ASN) remains insufficiently explored. The research to which this dataset is connected aims to illustrate some parameters of social activity around self-archived ORDs on ResearchGate. The study analyses whether the ORDs publication leads to social activity (reads and citations) around the ORDs and their linked published articles, including eventual associations between the social activity and the researchers’ profile (scientific domain, gender, region, professional position, reputation) as well as the quality of the ORD published.</p> <p>The current dataset is composed by:</p> <p>A- The .csv file, extracted as a random sample of 752 ORD items from ResearchGate. The dataset has been polished and anonymized. The variables relating the researchers' profiles and citations to the ORD lined research were got from the researchers' profiles and published research. However, for the purpose of anonymisation, these variables have been coded and the original information removed.</p> <p>B- The codebook explaining the variables and metrics contained in the file (A)</p> <p>C- The R script. This script contains a number of explorations not reported in the final paper. The quantitative techniques applied include descriptive statistics, logistic regression and K-means cluster analysis.</p> <p>D- Five tables, three figures and two annexes (Logistic Regression I and II) created over the basis of the dataset (A)</p> <p>The results have been interpreted in terms of three main aspects.</p> <ul> <li>Firstly, there is still an underdeveloped social activity around self-archived ORD in ResearchGate (operationalized as reads and citations) overall and in spite of the published ORDs quality.</li> <li>Secondly, it was found an uncovering of the relevance of the moderating effects over ORD, which spots traditional dynamics within the “innovative” practice of engaging with data practices.</li> <li>Thirdly, a rather similar situation of ResearchGate as ASN with regard to other data platforms and repositories in terms of social activity around ORD was detected.</li> </ul> <p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. </p>
Research Workflows and Open Science - Data Set
<p>Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.</p> <p>The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘<strong>workflow ID</strong>’ and ‘<strong>URL</strong>’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively.</p> <p>The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified:</p> <ul> <li> <p>'<strong>used/open science infrastructure/virtual</strong>'</p> <ul> <li> <p>If a workflow relies on a virtual open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open science infrastructure/physical</strong>'</p> <ul> <li> <p>If a workflow relies on a physical open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow relies on open source software (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open hardware</strong>'</p> <ul> <li> <p>If a workflow relies on open hardware (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow (re)uses open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow (re)uses open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/(open access) scientific publication</strong>'</p> <ul> <li> <p>If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>transparency/transparency type</strong>'</p> <ul> <li> <p>degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise)</p> </li> </ul> </li> <li> <p>'<strong>transparency/sharing type</strong>'</p> <ul> <li> <p>workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/collaboration implementation</strong>'</p> <ul> <li> <p>If a workflow implements collaborative practices (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdfunding</strong>'</p> <ul> <li> <p>If a workflow envisages crowdfunding (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdsourcing</strong>'</p> <ul> <li> <p>If a workflow envisages crowdsourcing (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/scientific volunteering</strong>'</p> <ul> <li> <p>If a workflow envisages scientific volunteering (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/citizen and participatory science</strong>'</p> <ul> <li> <p>If a workflow envisages citizen and participatory science (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/indigenous peoples</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/marginalised scholars</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/local communities</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with local communities (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>assessment</strong>'</p> <ul> <li> <p>If a workflow implements assessment processes for the evaluation of the research products created (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>automation</strong>'</p> <ul> <li> <p>If a workflow includes automated processes (yes/no)</p> </li> </ul> </li> </ul>
The LOTUS Initiative for Open Natural Products Research: TMAP
<p>TMAP of the compounds present on Wikidata curated in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a></p>
Open data and research tools
<p>From article: Government Innovation Laboratories in South America: Congruences and Peculiarities</p> <p>Journal: Brazilian Administration Review (BAR)</p>
Workflow for structured literature reviews using the Open Research Knowledge Graph (ORKG)
<p>Figure showing a workflow of making a structured literature review using the core features of the Open Research Knowledge Graph (ORKG). </p>
Survey to help identify whether Octopus might help researchers produce high quality, open research
<h2>Survey to help identify whether Octopus might help researchers produce high quality, open research</h2><p>In order to complement in-depth, but necessarily small sample size, interviews, an online survey allowed us to reach a broader population to gain some quantitative data on the current research culture and barriers to best practice, as well as whether the Octopus platform might help overcome them.</p><p>The finalised survey was implemented on EUSurvey. It is a fully open source online survey platform developed and administered by the European Commission, adhering to relevant privacy regulations (e.g. the General Data Protection Regulation (GDPR)).</p><p>The survey was open from 17 January to 5 February 2023.</p><p>Details on the implementation of this survey is published on Octopus.</p>
Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk Open-label Extension
ClinicalTrials.gov study NCT02867813. IPD Sharing: YES. Countries: 7. Publications: 2.
Open data for spatial public health research
Open the record for dataset details and reuse information.
Data from: Digitizing extant bat diversity: an open-access repository of 3D μCT-scanned skulls for research and education
Open the record for dataset details and reuse information.
Data from: Exploratory and confirmatory research in the open science era
Open the record for dataset details and reuse information.
OPEN-WINDOW: SOUND EVENT DATABASE FOR RESEARCH AND DEVELOPMENT
<p><strong>(1) Background:</strong></p> <p>Situated in the domain of urban sound scene classification by humans and machines, the research in this project will be a first step towards mapping urban noise pollution experienced indoors and finding ways to reduce its negative impact in peoples' homes. The acoustic distinction between outdoor and indoor scenes is an active research field and can be automated with some success. A much subtler difference is the change in the indoor soundscape induced by an open window. Being able to determine this, however, would allow applications in warning systems and be a prerequisite for an app-based urban sound mapping project.</p> <p>Acoustic detection requires neither line of sight nor sensors at the window frame or knowledge of the number of windows or their size. The task, however, varies substantially in difficulty with the amount of sound inside and outside. From the point of machine classification, the lack of specificity is the most problematic aspect: Very few sounds if any can be assumed to originate exclusively from outside <em>and</em> be present at all times to aid automatic detection. The required generalisation ability, however, can be assumed for humans, who might also use very subtle cues in the change of reverberations.</p> <p> </p> <p><strong>(2) Dataset</strong></p> <p><em>(a) Recording locations</em></p> <p>The recordings have been made at three different locations. </p> <ul> <li>Farm: A farm in Brook, Surrey, United Kingdom. The recordings were made in an open-plan studio flat area in the centre of the farm. The recordings in this location have the lowest levels of background noise, due mainly to a quiet environmental surrounding.</li> <li>Office 1: An office at the University of Surrey, Guildford, United Kingdom. The recordings were made in an open-plan office located on the first floor, at the Centre for Vision, Speech and Signal Processing (CVSSP). Since this office accommodates 16 researchers, recordings in this location have the highest level of background noise</li> <li>Office 2: An office at the University of Surrey, Guildford, United Kingdom. The recordings were made in a small size open-plan office at the CVSSP. This office accommodates 8 researchers and the recordings made in this office considered to have a medium level of background noise.</li> </ul> <p><em>(b) Recording equipment</em></p> <p>The recordings made at the two offices and a studio flat in a farm used a dedicated laptop, Focusrite Clarett 4pre USB external sound card (44,100 Hz sample rate at 16 bits per sample) 1, and a Behringer ECM 8000 microphone.</p> <p><em>(c) Recording setup</em></p> <p>The Behringer ECM 8000 microphone is connected to the External Line Return (XLR) input of the Focusrite Clarett external sound<br> card via an XLR cable. The external sound card is connected to the dedicated laptop and controlled using Ableton Live 10 software for setting configurations and exporting the recorded audio files. The microphone is located approximately 10 cm away from the<br> window and fixed using a microphone holder. At each location 90 audio sessions are recorded; 60 one minute recordings for static state setup and 30 fifteen seconds recordings for transitional state setup.</p> <p><em>(d) File naming conventions</em></p> <p>The naming convention for audio recording is as follows:<br> [Location] [State] [Time] [IDX]<br> [State] will be one of the following: “O stands for open, C stands for Close, OC means a transition from Open to Close and CO stands for a transition from Close to Open.” [Time] stamp will be one of the following: “AM stands for morning between 9:00 to 12:00, N stands for noon which is between 13:00 to 15:00 and PM which stands for an afternoon which is between 17:00 to 20:00.” [IDX] is<br> representing the file ID number. For example, “Farm C PM 01.wav”, means this file is recorded at the farm and in the afternoon when the window is closed and the file ID is 01.</p> <p><em>(e) Dataset acquisition:</em></p> <p>A recording kit consisting of a dedicated laptop and microphone will be given to volunteers. Custom-programmed software will remind the user to specify the window state (establishing the so-called ground truth).</p> <p><em>(f) Specifications</em><br> - Open-Window contains 270 audio recordings totalling 3.37 hours of audio.<br> - Each audio recording belongs to one of the four classes representing the window states; two stationary states (Open, Close) and two transitional states (Open-Close, Close-Open).<br> - The recordings were carried out in different locations and at different times of the day.<br> - Three locations: Office1, Office2, Farm<br> - Three periods of the day: Morning, Afternoon, Evening<br> - The recordings are split into six-folds.<br> - Fold 1 is the test set.<br> - Fold 2 is the validation set.<br> - Folds 3-6 comprise the training set.<br> Each fold is balanced in terms of the class and location distribution.<br> - The annotations/metadata can be found in annotations.csv.<br> - The recordings for the stationary states are approximately 60 seconds, while the recordings for the transitional states are approximate 15 seconds.<br> - The format of the recordings is 2-channel 16-bit PCM sampled at 44.1 kHz.</p>
Open Access to Excellent Science - the European Research Council's approach (video recording)
<p>Video recording of a presentation given at the event 'Open Access: Authors, publishers, investors and institutions for the dissemination of the scientific research's results' that was organised by the Library of EPFL in the context of the Open Access Week 2013. The video includes the introduction of the presentation by EPFL Director, Dr. Isabelle Kratz.</p> <p><strong>Abstract</strong>: We will give an overview of the ERC's approach to Open Access, and how it is reflected in the rules governing the ERC grants under the current 7th EU Research Framework Programme. We will explain how the ERC supports its grantees in providing open access to their research results, and shed some light on the changes to be expected for the new EU Research Framework Programme, Horizon 2020, which will start on 1 January 2014. In doing so, we will also take a look at the different roles of the ERC and the European Commission concerning the promotion of Open Access, and explain some of the differences in approach.</p>
Open Research Datasets – Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America
<p>Data supporting the findings of "Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America" by Victoria M. Fernandes, Gareth G. Roberts and Fred D. Richards. Further details about these data can be found in the main manuscript and accompanying Supporting Information. This research was funded by NERC Large Grant MC2: Mantle Convection Constrained (NE/T012595/1). <br><br>The datasets contained in this repository:<br>1) Uplift constraints from youngest outcropping marine to terrestrial stratigraphic transitions, as described in Fernandes et al. (2019) JGR Earth Surface (Figure 1; MarineTerrestrialTransition_Fernandes_etal2019.txt)<br>2) Digitised sediment isopachs from Roberts & Kirschbaum (1995), USGS Professional Paper 1561 (Supplementary Figure S3; Roberts_Kirschbaum_1995_isopachs.zip)<br>3) Air-loaded subsidence grids (Supplementary Figure S4; Subsidence.zip)<br>4) Temperature grids at 25 km depth intervals, from 50–400 km depth, generated from the SLNAAFSA hybrid Vs model of Hoggard et al. (2020), Nature Geoscience (Supplementary Figure S6; Temperature_grids_txt.zip)</p>
Data underlying research paper "Developing an open data intermediation business model: insights from the case of Esri"
<p><strong>Data underlying research paper “Developing an open data intermediation business model: insights from the case of Esri” </strong></p> <p>by Ashraf Shaharudin, Bastiaan van Loenen, and Marijn Janssen from Delft University of Technology (TU Delft), the Netherlands.</p> <p>This folder contains data underlying the research paper “Developing an open data intermediation business model: insights from the case of Esri”. It consists of:</p> <p>1. De-identified interview transcripts</p> <p>2. Informed consent form template</p> <p><strong>Note about the de-identified interview transcripts:</strong></p> <p>The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of Esri as open data intermediaries.</p> <p>The 27 interviews, involving 29 interviewees, were conducted between April 2023 and April 2024 based on the semi-structured approach. We shared the tentative interview questions with the interviewees in advance (for the majority, at least three working days prior). Since they are semi-structured interviews, the ultimate interview questions may differ from the tentative questions.</p> <p>We removed personally identifiable information from the transcripts. Some interviewees may risk being identifiable if their organization is known. Hence, we removed the organization and country information from all transcripts. </p> <p>With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible. </p> <p><strong>Note about the informed consent form template:</strong></p> <p>We sent the informed consent form to every interviewee in advance and requested that they return it to us before or during the interview. </p> <p>All interviewees whose interview transcripts are recorded in this document give permission for the anonymized transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.</p> <p><strong>Acknowledgement:</strong></p> <p>This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</p>
Dataset - Templates Recommendation in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing templates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>Two approaches have been trained on this dataset in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>, namely a Natural Language Inference (NLI) approach based on SciBERT embeddings and an unsupervised approach based on ElasticSearch.</p> <p> </p> <p>This publication consists therefore of one general dataset, two training sets for each approach, validation set for the supervised approach and a test set for both approaches.</p> <p> </p> <p><strong>dataset.json</strong></p> <p>The main JSON object consists of a list of templates and a list of neutral papers.</p> <p>Each template object has an ID, label, list of research fields, list of properties and list of papers using that template, whereas each paper object has ID, label, DOI, research field and abstract.</p> <p>Each neutral paper object has the same schema of a paper object using that template.</p> <p>See an example instance below.</p> <p> </p> <pre><code class="language-json">{ "templates": [ { "id": "R138668", "label": "Psychiatric Disorders AI Overview", "research_fields": [ { "id": "http://orkg.org/orkg/resource/R133", "label": "Artificial Intelligence" } ... ], "properties": [ "Study cohort", ... ], "papers": [ { "id": "R138698", "label": "Application of Autoencoder in Depression Diagnosis", "doi": "10.12783/dtcse/csma2017/17335", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Major depressive disorder (MDD) is a mental disorder characterized by at least two weeks of low mood which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. No method can automatically extract discriminative features from the origin time series in fMRI images for MDD diagnosis. In this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge. An autoencoder was used to learn pre-training parameters of a dimensionality reduction process using 3-D convolution network. Through comparison with the other three feature extraction methods, our method achieved the best classification performance. This method can be used not only in MDD diagnosis, but also other similar disorders." }, ... }, ... ] "neutral_papers": [ { "id": "R109377", "label": "Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants", "doi": "10.1016/j.jpha.2020.03.009", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Abstract The recent outbreak of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in December 2019 raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19." }, ... ] }</code></pre> <p> </p> <p><strong>All other files</strong></p> <p>The main JSON object consists of a list of entailments, a list of contradiction and a list of neutrals.</p> <p>Each object of the above mentioned lists has the same schema. An instance_id created by concatenating the template_id (when exists) with the paper_id, a template_id, a paper_id, premise (representing the paper's title), hypthesis (representing the paper's abstract), their concatenation in sequence and the target class.</p> <p>See an example instance below.</p> <p> </p> <pre><code class="language-json">{ "entailments": [ { "instance_id": "R138668xR138698", "template_id": "R138668", "paper_id": "R138698", "premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data", "hypothesis": "application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders", "sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders [SEP]", "target": "entailment" }, ... ], "contradictions": [ ... ], "neutrals": [ ... ] } </code></pre> <p> </p> <p><strong>Statistics</strong></p> <table align="center"> <tbody> <tr> <td>-</td> <td><strong>Training (supervised)</strong></td> <td><strong>Validation (supervised)</strong></td> <td><strong>Training (unsupervised)</strong></td> <td><strong>Test</strong></td> </tr> <tr> <td>Entailment</td> <td>180</td> <td>20</td> <td>200</td> <td>52</td> </tr> <tr> <td>Neutral</td> <td>180</td> <td>20</td> <td>200</td> <td>64</td> </tr> <tr> <td>Contradictrion</td> <td>736</td> <td>84</td> <td>0</td> <td>0</td> </tr> <tr> <td>Total</td> <td>1096</td> <td>124</td> <td>400</td> <td>116</td> </tr> </tbody> </table> <p> </p>
Results from CODATA-RDA Schools for Research Data Science "being open and responsible at home" exercise 2022
<p>This is an exercise in identifying potential influence of environmental factors on open and responsible research practices. Students were asked to write down concerns about conducting open and responsible research in their home environments (column 2), and to group these concerns around institutional/cultural issues, infrastructural issues and personal concerns (column 1). Through a facilitated discussion, the class identified tools, communities and resources to use to help overcome these concerns (column 3).</p>
Dataset and Research Questions for Open Science and Open Innovation
<p>Worksheet with a dataset of 55 articles and Research Questions in regard to bridging Open Science and Open Innovation.</p>
Supplementary material 1 from: Penev L (2017) From Open Access to Open Science from the viewpoint of a scholarly publisher. Research Ideas and Outcomes 3: e12265. https://doi.org/10.3897/rio.3.e12265
A presentation held by Lyubomir Penev in the iDiv Seminar Seies at the Biodiversity Informatics Unit of the German Centre for Integrative Biodiversity Research (iDiv) Leipzig, 15 February 2017.
Supplementary material 1 from: Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. https://doi.org/10.3897/rio.2.e8880
A translation of the proposal into French. IDRC operates in both English and French as a Canadian Crown Corporation.
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.