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5,061 results for “access”
Data for preprint: "Non-Telecentric 2P microscopy for 3D random access mesoscale imaging "
<p>Numerical data used in latest version of preprint: "Non-Telecentric 2P microscopy for 3D random access mesoscale imaging ", https://www.researchsquare.com/article/rs-121292/v1</p>
Interview with Peter Suber: Unlocking Knowledge for Social Equity (for International Open Access Week 2021)
<p>Interview with Peter Suber – Director of the Harvard Office for Scholarly Communication, Director of the Harvard Open Access Project, Senior Researcher at the Berkman Center for Internet & Society, and Professor of Philosophy Emeritus at Earlham College.</p> <p>In the view of many, Peter Suber has been a generous leader and guide not only among, but also to the brightest lights, clearest thinkers and most effective of collaborative practitioners in the Open Access to scholarly research movement - for at least the last two decades. In this wide ranging and timely interview for International Open Access Week 2021, Peter gives his expert, fact-based opinion on the origin, location and potential destination of various core elements of the Open Access movement and the scholarly communication system which it is so clearly shaping - now at speed.</p> <p>Interview topics covered include but are not limited to:</p> <ul> <li>A brief history of Open Access - Peter Suber’s Open Access story</li> <li>Open Access and its language defined</li> <li>Open Access highlights of the last "5,10 or 15" years: <ul> <li>Open Infrastructure <ul> <li>The Next Generation Repositories (NGR) project by the Coalition of Open Access Repositories (COAR) - over 4,000 institutional repositories in place to accelerate repository-based (Green) Open Access</li> </ul> </li> <li>Plan S and other big funders moves toward Open Access</li> <li>Transformative Agreements</li> <li>International responses to the Journal Impact Factor (JIF) <ul> <li>San Francisco DORA statement</li> <li>Leiden Manifesto</li> </ul> </li> <li>Controlled Digital Lending</li> </ul> </li> <li>Corporate capture of proprietary infrastructure and its scholarly content</li> <li>Structural equity – challenges and green shoots</li> <li>Open peer review</li> <li>The "perfect" Australian Open Access policy</li> </ul>
Maximizing Scholarly Access, Impact, and Visibility at the UW Law Library
<p>A video created by UW Law Librarians for the 2021 AALL Innovation Showcase highlighting how the UW Law Library helps UW Law faculty members increase the access, impact, and visibility of their scholarship.</p>
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
Data from: Size matters: when resource accessibility by ecosystem engineering elicits wood-boring beetle demographic responses
<p>This data was used to investigate how the age and size of beaver disturbances act as predictors for primary wood-boring beetle abundance and species richness around beaver-altered habitat patches. To do so, we sampled beetles around 16 beaver-disturbed and unaltered watercourses within the Kouchibouguac National Park (Canada) and modeled beetle demographical responses to site conditions and their physical characteristics, distance from the watercourse, deadwood biomass, and the geographical location of the sites.</p>
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
<p>Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p>SVP1 data missing for June 17-18 (2021) and July 5-7 (2021).</p> <p><br> Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) June 9-11 - 2021</p> <p>AIS recorded data not available after June 11, 2021</p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
Open Access Publishing
<p><strong>Open Access Publishing. </strong>Prepared for the Diploma in Education Technology Management (A Distance Education Programme) of NAAM, Hyderabad. DETM 524 – Ethical Issues in Educational Technology (3+0 credits) by Sridhar Gutam (<a href="mailto:sridhar.gutam@icar.gov.in">sridhar.gutam@icar.gov.in</a>) 2021</p> <p><strong>Video</strong>: <a href="https://youtu.be/A9Gxa6Z_0D8">https://youtu.be/A9Gxa6Z_0D8</a></p> <p><strong>Slide</strong> <strong>Deck</strong>: <a href="https://bit.ly/OAPSG">https://bit.ly/OAPSG</a></p>
Source data for "Non-Telecentric two-photon microscopy for 3D random access mesoscale 2 imaging"
<p>Source data used in a manuscript "Non-Telecentric two-photon microscopy for 3D random access mesoscale 2 imaging"</p> <p> </p> <p> </p> <p> </p>
Tree canopy accession strategy changes along the latitudinal gradient of temperate Northeast Asia
<p>Aim: Understanding how natural forest disturbances control tree regeneration is key to predict the consequences of globally accelerating forest diebacks on carbon stocks and forest biodiversity. Tropical cyclones (TCs) are important drivers of forest dynamics in Eastern Asia and it is predicted that their importance will increase. However, little is known about TC impact on forest regeneration.</p> <p>Location: Latitudinal gradient from south Korea (33°N) to the Russian Far East (45°N).</p> <p>Time period: Last 300 years.</p> <p>Major taxa studied: <i>Quercus mongolica</i>, <i>Abies nephrolepis</i> and <i>Pinus koraiensis</i>.</p> <p>Methods: We explore the effects of TC activity on canopy accession strategies derived from long-term tree radial growth patterns along a 1500-km latitudinal gradient of decreasing TC activity. We analyzed canopy accession strategies for more than 800 trees of three widely distributed tree species by dividing them into gap trees (GTs) that established immediately after gap formation, and released trees (RTs) that accessed the upper canopy after a period of competitive suppression.</p> <p>Results: We found a substantial decrease in GTs and increase in RTs proportionally along the gradient of decreasing TC activity. <i>P. koraiensis</i> and <i>A. nephrolepis</i> exhibited high variability in the proportions of the individual canopy accession strategies along the latitudinal gradient, while it was more stable for <i>Q. mongolica</i>. We identified the gradient of TC activity as the main driver influencing canopy dynamics and thus changes in life history traits for <i>P. koraiensis</i> and <i>Q. mongolica</i>, while maximal growth rate was the main driver for <i>A. nephrolepis</i>.</p> <p>Main conclusions: Flexibility in growth strategies enabled the studied species to cover extensive areas and indicates that they will be able to cope with shifts in disturbance regimes induced by the poleward migration of TCs and increasing TC intensity. Our results highlight the canopy accession strategy as an ecological indicator of past disturbance activity.</p>
Genetic and fitness measurements of Brighamia accessions for APPS
<p><span><span><strong>Premise of the study:</strong><i> </i>Living collections maintained for generations are at risk of diversity loss, inbreeding, and adaptation to cultivation. To address these concerns the zoo community uses pedigrees to track individuals and implement crosses that maximize founder contributions and minimize inbreeding. Using a pedigree management approach in an exceptional plant, we demonstrate how conducting such strategic crosses can minimize genetic issues that have arisen under current practices. </span></span></p> <p><span><span><strong>Methods:</strong> We performed crosses between these collections and compared the fitness of progeny, including plant performance and reproductive health. We genotyped the progeny and paternal accessions to measure changes in diversity and relatedness within and between accessions. </span></span></p> <p><span><span><strong>Results:</strong><i> </i>The mean relatedness among individuals of an accession, suggests they are full siblings. As a result there was high inbreeding and low diversity within an accession, although less so among accessions. Progeny from the wider crosses had increased genetic diversity, while selfed accessions were smaller and less fertile. </span></span></p> <p><span><span><strong>Discussion:</strong> Institutions which hold exceptional species should consider how diversity is maintained within their collections. Implementing a pedigree-based approach to managing reproduction of ex situ plants will slow the inevitable loss of genetic diversity and in turn, result in healthier collections. </span></span></p>
Adiabatic and diabatic signatures of ocean temperature variability - ACCESS-CM2 processing/plotting code and processed data
<p>Contains processed data and processing/plotting code for the figures in the article:</p> <p>Holmes, R.M., Sohail, T. and Zika, J.D. (2022): Adiabatic and diabatic signatures of ocean temperature variability, Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-21-0695.1">https://doi.org/10.1175/JCLI-D-21-0695.1</a></p> <p>For more information please see the published article, as well as the github repository where the code is described in more detail: <a href="https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695">https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695</a></p>
Modelling and Enforcing Access Control Requirements for Smart Contracts - Data Set
<p>This data set contains all artifacts for the master thesis of Jan-Philipp Töberg at the Karlsruher Institute of Technology. This includes the Eclipse project for the metamodel and the generator, the extension of the Slither framework and the use case instances employed during the evaluation. Additionally, extensive instructions regarding the installation and usage are provided.</p>
CamoEvo: an open access toolbox for artificial camouflage evolution experiments
<p>Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of colour patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimisation by using tailored genetic algorithms, animal and egg maculation theory and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (~1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimised to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and genetic algorithm as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer based phenotype optimisation experiments.</p>
TRINITY open access data repository data set 2 by Budapest University of Technology and Economics
<p>Horizon 2020 programme supports access to and reuse of research data generated by Horizon 2020 projects through the Open Research Data Pilot (ORDP). To support the validation of scientific results, the pilot focuses on providing access to data needed to validate the scientific results. There are several types of such data, e.g. machine learning data sets, models, measurements, statistical results of experiments, survey outcomes, etc.</p> <p>This deliverable summarizes the data that are expected to be collected in the course of the project and where and how they are stored. The aspect of providing open access to research data (as required by the European Commission’s Open Research Data Pilot, <a href="https://www.openaire.eu/what-is-the-open-research-data-pilot">https://www.openaire.eu/what-is-the-open-research-data-pilot</a>) is addressed in Section 3. Finally, in Section 4 we describe the data sets that were or are expected to be generated within the TRINITY projects and made freely available.</p>
TRINITY open access data repository by Budapest University of Technology and Economics
<p>Horizon 2020 programme supports access to and reuse of research data generated by Horizon 2020 projects through the Open Research Data Pilot (ORDP). To support the validation of scientific results, the pilot focuses on providing access to data needed to validate the scientific results. There are several types of such data, e.g. machine learning data sets, models, measurements, statistical results of experiments, survey outcomes, etc.</p> <p>This deliverable summarizes the data that are expected to be collected in the course of the project and where and how they are stored. The aspect of providing open access to research data (as required by the European Commission’s Open Research Data Pilot, <a href="https://www.openaire.eu/what-is-the-open-research-data-pilot">https://www.openaire.eu/what-is-the-open-research-data-pilot</a>) is addressed in Section 3. Finally, in Section 4 we describe the data sets that were or are expected to be generated within the TRINITY projects and made freely available.</p>
Image Dataset of Accessibility Barriers
<p><strong>The Data</strong><br> The dataset consist of 5538 images of public spaces, annotated with steps, stairs, ramps and grab bars for stairs and ramps. The dataset has annotations 3564 of steps, 1492 of stairs, 143 of ramps and 922 of grab bars.</p> <p>Each step annotation is attributed with an estimate of the height of the step, as falling into one of three categories: less than 3cm, 3cm to 7cm or more than 7cm. Additionally it is attributed with a 'type', with the possibilities 'doorstep', 'curb' or 'other'.</p> <p>Stair annotations are attributed with the number of steps in the stair.</p> <p>Ramps are attributed with an estimate of their width, also falling into three categories: less than 50cm, 50cm to 100cm and more than 100cm.</p> <p>In order to preserve all additional attributes of the labels, the data is published in the CVAT XML format for images.</p> <p> </p> <p><strong>Annotating Process</strong><br> The labelling has been done using bounding boxes around the objects. This format is compatible with many popular object detection models, e.g. the YOLO object model. A bounding box is placed so it contains exactly <em>the visible part</em> of the respective objects. This implies that only objects that are visible in the photo are annotated. This means in particular a photo of a stair or step from above, where the object cannot be seen, have not been annotated, even when a human viewer can possibly infer that there is a stair or a step from other features in the photo.</p> <p><strong>Steps</strong><br> A step is annotated, when there is an vertical increment that functions as a passage between two surface areas intended human or vehicle traffic. This means that we have not included:</p> <ul> <li>Increments that are to high to reasonably be considered at passage.</li> <li>Increments that does not lead to a surface intended for human or vehicle traffic, e.g. a 'step' in front of a wall or a curb in front of a bush.</li> </ul> <p>In particular, the bounding box of a step object contains exactly the incremental part of the step, but does not extend into the top or bottom horizontal surface any more than necessary to enclose entirely the incremental part. This has been chosen for consistency reasons, as including parts of the horizontal surfaces would imply a non-trivial choice of how much to include, which we deemed would most likely lead to more inconstistent annotations.</p> <p>The height of the steps are estimated by the annotators, and are therefore not guarranteed to be accurate.</p> <p>The type of the steps typically fall into the category 'doorstep' or 'curb'. Steps that are in a doorway, entrance or likewise are attributed as doorsteps. We also include in this category steps that are immediately leading to a doorway within a proximity of 1-2m. Steps between different types of pathways, e.g. between streets and sidewalks, are annotated as curbs. Any other type of step are annotated with 'other'. Many of the 'other' steps are for example steps to terraces.</p> <p><strong>Stairs</strong><br> The stair label is used whenever two or more steps directly follow each other in a consistent pattern. All vertical increments are enclosed in the bounding box, as well as intermediate surfaces of the steps. However the top and bottom surface is not included more than necessary for the same reason as for steps, as described in the previous section.</p> <p>The annotator counts the number of steps, and attribute this to the stair object label.</p> <p><strong>Ramps</strong><br> Ramps have been annotated when a sloped passage way has been placed or built to connect two surface areas intended for human or vehicle traffic. This implies the same considerations as with steps. Alike also only the sloped part of a ramp is annotated, not including the bottom or top surface area.</p> <p>For each ramp, the annotator makes an assessment of the width of the ramp in three categories: less than 50cm, 50cm to 100cm and more than 100cm. This parameter is visually hard to assess, and sometimes impossible due to the view of the ramp.</p> <p><strong>Grab Bars</strong><br> Grab bars are annotated for hand rails and similar that are in direct connection to a stair or a ramp. While horizontal grab bars could also have been included, this was omitted due to the implied ambiguities of fences and similar objects. As the grab bar was originally intended as an attributal information to stairs and ramps, we chose to keep this focus. The bounding box encloses the part of the grab bar that functions as a hand rail for the stair or ramp.</p> <p> </p> <p><strong>Usage</strong><br> As is often the case when annotating data, much information depends on the subjective assessment of the annotator. As each data point in this dataset has been annotated only by one person, caution should be taken if the data is applied.</p> <p>Generally speaking, the mindset and usage guiding the annotations have been wheelchair accessibility. While we have strived to annotate at an object level, hopefully making the data more widely applicable than this, we state this explicitly as it may have swayed untrivial annotation choices.</p> <p>The attributal data, such as step height or ramp width are highly subjective estimations. We still provide this data to give a post-hoc method to adjust which annotations to use. E.g. for some purposes, one may be interested in detecting only steps that are indeed more than 3cm. The attributal data makes it possible to sort away the steps less than 3cm, so a machine learning algorithm can be trained on this more appropriate dataset for that use case. We stress however, that one cannot expect to train accurate machine learning algorithms inferring the attributal data, as this is not accurate data in the first place.</p> <p>We hope this dataset will be a useful building block in the endeavours for automating barrier detection and documentation.</p>
WikiProject Clinical Trials for multilingual access to information
<p>Watch at <a href="https://www.youtube.com/watch?v=uJbn0dPqAE8">https://www.youtube.com/watch?v=uJbn0dPqAE8</a></p> <p>WikiProject Clinical Trials is a wiki community project to increase access to medical research metadata. Check it out at <a href="https://www.wikidata.org/wiki/Wikidata:WikiProject_Clinical_Trials">https://www.wikidata.org/wiki/Wikidata:WikiProject_Clinical_Trials</a></p> <p>Here I argue that everyone has a right to access medical research metadata and that for public interest, we need to translate basic information from trials into many languages. I piloted this process in Wikidata.</p>
Selection strategies to introgress water deficit tolerance derived from Solanum galapagense accession LA1141 into cultivated tomato (datasets)
<p>This dataset includes best linear unbiased predictors (BLUPs) used for composite interval mapping in the LA1141 × OH8245 BC<sub>2</sub>S<sub>3</sub> families (tab - BC2S3_BLUP_data_for_CIM), greenhouse data corresponding to the BC<sub>2</sub>S<sub>5</sub> advanced lines (tab - BC2S5_GH_trial), and field performance data corresponding to the BC<sub>2</sub>S<sub>5</sub> advanced lines (tab - BC2S5_Field_Trial).</p>
Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations.
<p>This dataset underpins the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations".</p> <p>The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total).</p> <p>An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs. </p> <p>The results can be found https://doi.org/10.5281/zenodo.6468262</p>
Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations
<p>The attached modeling results underpin the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal electricity access (SDG7) in forty-eight African nations".</p> <p>The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total).</p> <p>An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs. </p> <p>The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation.</p> <p>The OSeMOSYS model and input data used to produce these results can be found at JoPapp/jrc_temba: v1.0.2 [Data set]. Zenodo.https://doi.org/10.5281/zenodo.6468278 (Authors: Ioannis Pappis. (2021)).</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.