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5,061 results for “access”
Learned value modulates the access to visual awareness during continuous flash suppression
<p>Data from Experiment 1 and Experiment 2 are reported in separate files. </p> <p>Each line contains the mean suppression time of a target grating under continuous flash suppression expressed in seconds for one participant. </p> <p>Each column refers to a different condition:<br> HREV = visual stimuli associated with high monetary reward<br> LREV = visual stimuli associated with low monetary reward<br> base = baseline measurements before associative learning<br> P1 = first measurement after associative learning<br> P2 = second measurement after associative learning<br> P3 = third measurement after associative learning</p> <p>For experiment 1, a short (20 trials) associative learning recall session was performed between P1 and P2 and between P2 and P3.</p> <p> </p> <p> </p> <p> </p>
ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data and k-means analysis used for the study described in Fiddes et al. 2022 '<em>Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?' .</em> </p> <p>Included files: </p> <ul> <li>modis_cluster_centres_2015-2019.nc - kmeans derived cluster centres for MODIS</li> <li>modis_cluster_labels_2015-2019.nc - kmeans derived cluster labels for MODIS </li> <li>bx400_cluster_labels_2015-2019.nc - kmeans fitted cluster label for model </li> <li>COSP_vars_bx400_2015-2019.nc - model data for analysis </li> </ul> <p>The code that performs the analysis/generates this data and has instructions for where to download MODIS data can be found here: https://github.com/sfiddes/code_for_publications_2022/tree/main/ACCESS_cloud_radiation_eval</p>
DOIs linked by the English Wikipedia which could be made available in green Open Access
<p>List of citations from the English Wikipedia articles extracted from the enwiki-20170720-pages-articles XML dump via https://pypi.org/project/mwcites/ , DOIs cleaned with custom regular expressions.</p> <p>The list of scholarly publications identified by the DOIs has been filtered to exclude those which are already available in Open Access and those which may not be depositable according to SHERPA/RoMEO policy summaries, first via the Dissemin API and then by the oaDOI API, with the attached Python script (https://github.com/nemobis/bots/blob/master/doi-doai-openaccess.py ).</p> <p>This produced a list of 194913 DOIs available in open access and 430230 DOIs unavailable and depositable (as of 2017-08-22 data, which for oaDOI was partly v1 and partly v2).</p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
Improving access to and reuse of research results, publications and data for scientific purposes - Stakeholders' consultations results
<p>The data sets were created via data collection effort for the Horizon Europe-funded "study to evaluate the effects of the EU copyright framework on research and the effects of potential interventions and to identify and present relevant provisions for research in EU data and digital legislation, with a focus on rights and obligations". The study was contacted by DG RTD. </p> <p>This research project supports Action 2 objectives of the European Research Area (ERA) Policy Agenda 2022-2024, which aims to propose an EU legislative and regulatory framework for copyright and data that is fit for research. The report provides a comprehensive analysis of barriers to the access and reuse of publicly funded research, including scientific publications and data. It assesses existing EU copyright legislation and EU data and digital legislation. It also assesses regulatory frameworks and national initiatives and identifies potential areas for improvement.</p> <p>Using a methodological, evidence-based approach (including the survey results posted in this repository), the study presents possible legislative and non-legislative measures to improve the current EU copyright and data framework and align it with the needs of scientific research and open research data principles. </p> <p>The data sets include the raw data of the three surveys (survey 1 targeted at researchers, survey 2 targeted at research-performing organisations, and survey 3 targeted at publishers). All surveys have two major parts: one concerning copyright legislation and another concerning data and digital legislation. In addition, we provide interview notes, they are also organised into two parts: one concerning copyright legislation and another concerning data and digital legislation. </p> <p>The data collection effort was partially supported by our colleagues from the Institute for Information Law (IVIR) and KU Leuven CiTIP. </p>
ACCESS-AM2 model output for 2017-2018 MARCUS and 2018-2019 CAMMPCAN RSV Aurora Australis voyages
<p>The dataset includes model output from the ACCESS-AM2 model corresponding to the MARCUS (Measurements of Aerosols, Radiation and Clouds over the Southern Oceans) 2017-2018 voyages and the CAMMPCAN (Chemical and Mesoscale Mechanisms of Polar Cell Aerosol Nucleation) 2018-2019 voyages. The MARCUS voyages included a limited number of CAMMPCAN instruments while the CAMMPCAN voyages included the full suite of instruments. </p> <p>The model version used was ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) run with CMIP6 AMIP configuration, nudged with ERA5 reanalysis and full chemistry switched on. The model was configured with a horizontal resolution of 1.25◦ latitude and 1.875◦ longitude and 85 vertical levels. ACCESS-AM2 uses the UK Met Office’s Unified Model Global Atmosphere (UM10.6 GA7.1) as the atmosphere module, the Community Atmosphere Biosphere Land Exchange model version 2.5 (CABLE2.5) as the land-surface module and the Global Model of Aerosol Processes (GLOMAP-mode) as the aerosol module. More information on the ACCESS-AM2 model can be found at <a href="https://doi.org/10.1071/ES19033">https://doi.org/10.1071/ES19033</a>.</p> <p>The data is at daily means spanning 29-10-2017 to 26-03-2018 (149 days) for MARCUS, and 25-10-2018 to 24-03-2019 (151 days) for CAMMPCAN.</p> <p>Files included in this upload include:</p> <ul> <li>aa1718_cg893_track.nc: aerosol, chemistry, and meteorology model data for the MARCUS voyages</li> <li>cg893_daily_mean_MARCUS_size_distributions.nc: calculated aerosol size distribution model data for the MARCUS voyages </li> <li>aa1819_cg893_track.nc: aerosol, chemistry, and meteorology model data for the CAMMPCAN voyages</li> <li>cg893_daily_mean_CC_size_distributions.nc: calculated aerosol size distribution model data for the CAMMPCAN voyages</li> </ul> <p>File names refer to Aurora Australis, voyage years (either 2017-2018 or 2018-2019), followed by the model run and data type.</p> <p>An overview of the variable field names, variable long names, height profile availability and units available in the dataset is provided in VariablesOverview.xlsx. </p> <p>A Jupyter Notebook is also included and contains scripts that can be used to create figures for preliminary analysis using the model data.</p> <p>Additional information:</p> <ul> <li>MARCUS details: <a href="https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf">https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf</a></li> <li>CAMMPCAN details: <a href="https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation">https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation</a></li> <li>MARCUS Observations: <a href="https://doi.org/10.26179/5e54ab5e5d56f">https://doi.org/10.26179/5e54ab5e5d56f</a></li> <li>CAMMPCAN Observations: <a href="https://doi.org/10.26179/5e546f452145d">https://doi.org/10.26179/5e546f452145d</a> </li> </ul> <p>The GitHub repository containing the code used to produce these datasets can be found here: <a href="https://github.com/llamprey/aurora_voyages">https://github.com/llamprey/aurora_voyages</a></p> <p> </p> <p>Versions:</p> <p>1.0.0: Initial Version.</p> <p>1.1.0: Fixed bug where CN and CCN fields were incorrectly calculated.</p> <p>1.2.0: Updated aerosol size distribution files</p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Rare Diseases
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "Alternative innovation models of pharmaceutical development for rare disease drugs: how (and) do they work?: A qualitative study". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 10/11 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p> <p>Details about the research project can be found at: <a href="https://www.graduateinstitute.ch/NBM">https://www.graduateinstitute.ch/NBM</a></p>
PhysiCell Studio: a graphical tool to make agent-based modeling more accessible. Supplemental material.
<p>Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification, i.e., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged. This dataset provides Supplemental material for the PhysiCell Studio publication.</p>
2023 Utrecht University Open Access Monitor (peer reviewed journal articles)
<p>Results of the OA monitor of Utrecht University (UU) and University Medical Center Utrecht(UMCU) for the year 2023. It lists the open access availability of all peer reviewed journal articles registered in the CRIS (Pure) of Utrecht University and/or University Medical Center Utrecht. </p>
Interviews with editors of library science journals on transitioning to open access
<p>These three files are related to qualitative, semi-structured interviews conducted in Fall 2023 with editors of Library and Information Science (LIS) journals on transitioning to open access. One subgroup consisted of participants who were editors at the time of an LIS journal when it transitioned (or flipped) to an open access model that does not charge a fee to either readers or authors (which this study refers to as equitable open access), and the other subgroup consisted of current editors (at the time) of LIS journals that have not yet transitioned (or unflipped) to an equitable open access model. Two of the files are the interview protocols for each group of flipped and unflipped editors, and the third file is the codebook the researchers used to analyze the interview transcripts. Interview transcripts are not being publicly shared to ensure confidentiality for interview participants.</p> <p>The interview protocols were created based on the findings of a prior research study:</p> <p>Borchardt, R., Dawson, D., & Schultz, T. (2024). Financial and other perceived barriers to transitioning to an equitable no-publishing fee open access model: A survey of LIS journal editors. College & Research Libraries, 85(1). <a href="https://doi.org/10.5860/crl.85.1.96">https://doi.org/10.5860/crl.85.1.96</a></p> <p>The codebook was created iteratively based on the researchers' review and analysis of the interview transcripts.</p>
Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"
<p>Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in <em>Frontiers in Marine Science</em></p>
The Effect of Prescription Drug Monitoring Programs (PDMPs) and Overdose Reversal Drug Accessibility Laws on Opioid Analgesic Mortality in the United States
<p><strong>Purpose: </strong>This analysis focuses on the impact of Prescription Drug Monitoring Programs (PDMPs) and increased layperson access to the overdose reversal drug Naloxone on prescription opioid mortality rates. The prescription opioid mortality rate was analyzed against state laws governing PDMPs and Naloxone accessibility to laypersons to evaluate if there was a correlation between mortality reduction and implementation of the laws.</p> <p>Three main analyses were conducted:</p> <ol> <li>Does a state’s opioid mortality reduction correlate to an effective PDMP and/or Naloxone law?</li> <li>Do states with strong PDMP laws have a corresponding opioid overdose mortality rate reduction?</li> <li>Do states with strong Naloxone accessibility laws have a corresponding opioid overdose mortality rate reduction?</li> </ol> <p><strong>Conclusion:</strong> Patterns show that PDMP and Naloxone accessibility can be successful in reducing mortality rates, but implementations and results vary dramatically between states. Further changes to both PDMPs and Naloxone accessibility are needed for states to see reliable and consistent mortality reductions. Changes to regulations need time to implement and take effect, meaning longer term measurements and data will be required to see if positive impacts can be sustained.</p> <ol> </ol> <p><strong>Data and Datasets:</strong></p> <p>There are three main datasets used in the analysis.</p> <ul> <li>Opioid mortality rate by state from 2006 to 2016. <ul> <li>This datasetis from the CDC website (<a href="https://www.cdc.gov/drugoverdose/data/statedeaths.html">https://www.cdc.gov/drugoverdose/data/statedeaths.html</a>). I used web scraping and regular expression search to extract the data and calculated mortality reduction of each state.</li> <li><em>Mortality reduction formula: (Highest mortality rate) – (2016 mortality rate)</em><br> </li> </ul> </li> <li>PDMP law implementation in each state and its timelinefrom January 1, 1998 to July 1, 2016. <ul> <li>Dataset is from the Prescription Drug Abuse Policy System(<a href="http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372">http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372</a>). This dataset encompasses laws regulating which professions have access to the database and for what purpose, whether practitioners can delegate their access, whether patients can see their own information, and the extent to which access to individually-identified records may be granted for law enforcement purposes.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing the PDMP.I performed the following data preparation to allow for analysis and visualization:</li> <li><em>PDMP law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul> <ul> <li>Naloxone accessibility to laypersons law implementation in each state and its timeline from January 1, 2001 to December 31, 2016. <ul> <li>Dataset is also from the Prescription Drug Abuse Policy System (<a href="http://pdaps.org./datasets/laws-regulating-administration-of-naloxone-1501695139">http://pdaps.org./datasets/laws-regulating-administration-of-Naloxone-1501695139</a>).This dataset focuses on state laws that provide civil or criminal immunity to licensed healthcare providers or lay responders for opioid antagonist administration.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing Naloxone accessibility to laypersons. I performed the following data preparation to allow for analysis and visualization:</li> <li><em>Naloxone accessibility law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul>
Open Access levels of Dutch universities' output 2016-2017 (articles & reviews): green, gold, hybrid and bronze - May 2018
<p>Using Web of Science and Unpaywall data, we here provide an update of Open Access (OA) levels of Dutch universities, for 2016 and 2017.</p> <p>Our previous analysis (<a href="http://doi.org/10.5281/zenodo.1133759">10.5281/zenodo.1133759</a> and <a href="http://doi.org/10.7287/peerj.preprints.3520v1">10.7287/peerj.preprints.3520v1</a>) looked at OA classification as included in Web of Science (gold and green OA, based on Unpaywall data), and supplemented that with a breakdown of gold OA into pure gold, hybrid and bronze, taken from Unpaywall data (formerly OADOI) directly. Here, we improve on this by running all DOIs retrieved from WoS through Unpaywall data (using their web interface that allows batch checking of up to 10,000 DOIs at a time). Unlike WoS, Unpaywall data itself includes author-submitted versions in their green OA classification, resulting in more complete green OA levels. </p> <p>In addition, since our initial analysis of December 2017, Unpaywall data has considerably expanded its coverage of institutional repositories (IRs) (see <a href="https://unpaywall.org/sources">https://unpaywall.org/sources</a>). This now includes coverage of the IRs from all Dutch universities. </p> <p>Taken together, the current data show higher levels of green open access, including author-submitted versions, compared to our previous analysis. </p> <p>In this update, we include output (articles and reviews) from 2016 and 2017 for all 14 universities in the Netherlands. </p> <p>The following categories are distinguished (description taken from Piwowar at al., 2018, doi: <a href="https://doi.org/10.7717/peerj.4375">10.7717/peerj.4375</a>)</p> <ul> <li><strong>Pure gold</strong>: Published in an open-access journal (as defined by the DOAJ)</li> <li><strong>Hybrid</strong>: Free under an open license in a toll-access journal</li> <li><strong>Bronze</strong>: Free to read on the publisher page, but without a license</li> <li><strong>Green: </strong>Available from an institutional or disciplinary repository (including PubMedCentral)</li> </ul> <p>Data for Dutch universities were collected from Web of Science using the organization-enhanced field. Only articles and reviews were included. DOIs were extracted from the Web of Science export, run through the Unpaywall data <a href="https://unpaywall.org/products/simple-query-tool">Simple Query Tool</a>. From the resulting data from Unpaywall, OA classification was done using a simple formula in Excel (to be replaced by an R script in a future update). The Excel template used is included in this dataset, as is the OADOI API output for each Dutch university's article subset, and the lists of DOIs derived from Web of Science. The dataset also includes summarized data and three charts generated from these data, showing levels of different types of OA for 2016, 2017 and the two years compared. </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Method Classification of Open Access INTACT Molecular Interaction data.
<p>Simple classification data derived from open access papers indexed in the INTACT database (https://www.ebi.ac.uk/intact/downloads) based on PSI-MI25 codes for interaction detection methods or participant detection methods based on the subfigure caption text. <br> <br> intact_records_and_captions_complete.tsv - This file links available text of subfigure captions to PSI-MI25 codes for the interaction detection method and participant detection method. </p> <p>evidx_run_file.txt - This file provides execution codes for the 'EvidX' machine learning text classifier (https://github.com/SciKnowEngine/evidX/releases/tag/v0.1.0)</p> <p> </p> <p> </p> <p> </p>
Smart Locks Access Control System
<p>The smart locks access system (Outlock’s locking units) is based on the patented Knock Code technology, which transfers encrypted data by mechanical pulses to unlock the lock. Since there are no conventional keys, keyholes or external parts, locking units are highly resistant to break-ins, vandalism, and the toughest weather conditions. To unlock the lock, the user enters the code and simply holds the KnockKey against the opening’s surface. Additionally, LNLI lock application provides a sophisticated electronic locks’ management system.</p>
A Role-Based Access Control model in Modbus Scada systems. A centralized model approach
<p>A Role-Based Access Control model in Modbus Scada systems. A centralized model approach. The files included are:</p> <ul> <li>ASA configuration</li> <li>Router1 configuration</li> <li>Router2 configuration</li> <li>Router3 configuration</li> <li>RoleDB</li> <li>openssl.cnf arbitrary extension file</li> </ul>
Toolkit on Open Access for Research Project Coordinators
<p>The materials in this toolkit were created by Romain Féret as a resource for training on how to help project coordinators to comply with their open access requirements. The slides of the training are available on Zenodo at 10.5281/zenodo.3381783. This training day took place on Wednesday the 5th of June 2019, at the University of Lille. It was organized with the support of Couperin as a part of its activities in the project OpenAIRE-Advanced.</p> <p>The tutorials are divided into two folders. The ‘Coordinator’ folder contains documents that can be sent directly to the researchers, while the ‘Support staff’ folder contains tutorials for support staff (librarians, project managers) who help the coordinators to manage their project. Each tutorial is in .pdf and .docx format for easy reuse and modification. Each document is available in French and in English.</p>
EXPLORATORY SPATIAL ANALYSIS OF "ACCESS" TO PHYSICAL AND DIGITAL RETAIL BANKING CHANNELS IN THE UK
<p>File built in order to explore access to banking channels in the UK (February 2019)</p> <p>The report "Exploratory Spatial Analysis of Access to Physical and Digital Retail Banking Channels in the UK" has been published by Think Forward Initiative in October 2019. You can download the full report from here: <a href="https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk">https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk</a></p> <p>Related code: <a href="https://github.com/andrasonea">andrasonea</a>/<strong><a href="https://github.com/andrasonea/TFI_AccessToBanking">TFI_AccessToBanking</a></strong></p> <p> </p>
Generation of synthetic, realistic vehicular traces for three access highways of Quito using SUMO
<p>These files present the maps of three access highways of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in July 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles for each road considerings the statistics from traffic authority of Quito</li> <li>Configuration of the 5 generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular traces</li> </ul>
La piattaforma di riviste Open Access dell'Università degli Studi di Milano. [Video]
<p>L’intervento percorre i passi fondamentali della creazione della piattaforma di riviste open access dell’Università degli Studi di Milano, come modello in cui una istituzione che produce conoscenza decide di assumersi la responsabilità di validare e diffondere questa conoscenza ad un pubblico che sia il più ampio possibile, liberandosi da scelte e vincoli imposti dagli editori commerciali e dalle logiche editoriali e riportando nelle mani dei ricercatori le attività che da tempo erano state consegnate agli editori.</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.