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5,061 results for “access”
ATMO-ACCESS WP2_Success Story_TNA PIRAThe
<p>Users across Europe and beyond have used ATMO-ACCESS services to enhance their research projects. We are collecting stories from users to illustrate their access experience and make the benefits of transnational access more tangible to new users.</p> <p>In this video, you will discover Martin Ramacher - Postdoctoral scientist in the Department of Chemical Transport Modelling at the <a href="https://www.linkedin.com/company/hereon-helmholtz/" target="_self">Helmholtz-Zentrum Hereon-</a>'s ATMO-ACCESS success story. He explains his project PIRAThe " Particulates infiltrating residencies in Athens" undertaken at ATMOS, National observatory of Athens. You will learn how this groundbreaking European project facilitates access to atmospheric research infrastructures and propels scientific innovation.</p> <p>On the programme:</p> <ul> <li>Dr. Martin Ramacher's entertaining testimonial</li> <li>Overview of the scientific results made possible by ATMO-ACCESS</li> <li>Focus on an exposure study based on indoor environment</li> </ul> <p>Find out more here : <a href="https://www.atmo-access.eu/meetourusers/">https://www.atmo-access.eu/meetourusers/</a></p>
Scream's roughness grants privileged access to the brain during sleep. - audio files
<p>Audio files played during the experiment described in:</p> <pre>https://doi.org/10.5281/zenodo.8407716</pre> <p>Audio files are identified as follows: N_cond_X_st_Y.wav.</p> <p>X corresponds to the condition, where 1 represents a scream and 2 represents a neutral vocalization.</p> <p>Y corresponds to the identifier of the actor who produced the vocalization.</p> <p> </p>
Byproduct-to-host ratios for assessing the accessibility of mineral resources
<p>This repository contains the supplementary information files of the article "Byproduct-to-host ratios for assessing the accessibility of mineral resources", published in the journal Environmental Sciences & Technology. This version of SI files is more documented than the previous one and with reference added to the article.</p> <ul> <li>"SI_1_BtH_ratios_v0.1.xlsx" contains both input data and results of the article</li> <li>"SI_2_Historic_prices.xlsx" contains the historical market price of mineral resources covered in the study</li> <li>"SI_3_Representavity_dataset.xlsx" containts the dataset required to evaluate the representativity of the dataset with regards to alternative estimates in the literature</li> <li>"SI_4_RR_LitReview.xlsx" show the data collected during the literature review of minerals recovery rates along global supply chains</li> <li>"SI_5_Production_2021.xlsx" provides the primary production of minerals in 2021</li> <li>"SI_6_Host_byproduct_Greffe2024.docx" provides additional information on the methodology and data collection</li> <li>"SI_7_Representativity_results.xlsx" contains the output results of the representativity check, using data from supporting information 1 and supporting information 3</li> </ul> <p>BtH ratios are obtained using "ResC" data in "SI_1_BtH_ratios_v0.1.xlsx" and using the byproduct_host_ratio python class available at: https://github.com/TitouanGreffe/BtH_ratios</p> <p>Article here: <a title="DOI URL" href="https://doi.org/10.1021/acs.est.4c05293">https://doi.org/10.1021/acs.est.4c05293</a></p>
Great Britain Accessibility Indicators 2023 (AI23)
<p><strong>Overview</strong></p> <p>Accessibility indicators measure the ease of reaching valuable destinations (Levinson and Wu 2020). The current dataset, Great Britain Accessibility Indicators 2023 (AI23), provides small-area indicators to key services such as health, education, employment, and urban centres.</p> <p>This dataset is an updated and expanded version of the the <a href="https://zenodo.org/records/8037156">Public Transport Accessibility Indicators for Great Britain 2022</a> (PTAI22) dataset (available at: https://zenodo.org/records/8037156, and described at: https://www.nature.com/articles/s41597-023-02890-w) (J. Rafael Verduzco Torres and McArthur 2024).</p> <p>In particular, the AI23 dataset provides a suite of ready-to-use accessibility indicators by public transport,<br>bicycle, and on foot to employment, general practices (GPs), hospitals, pharmacies, parks and gardens,<br>primary and secondary schools, supermarkets, main urban centres, and urban sub-centres. These indicators<br>are available for 42,000 small area units across Great Britain (GB), specifically at the Lower Super Output<br>Area (LSOA) level in England and Wales, and the Data Zone (DZ) level in Scotland. It includes active modes, specifically walking and cycling, in addition to public transport.</p> <p>A full data descriptor is available <a href="https://osf.io/preprints/socarxiv/qb9j4_v1">here</a> (URL: https://osf.io/preprints/socarxiv/qb9j4_v1) (Verduzco Torres & McArthur, 2024b).</p> <p> </p> <p><strong>Records</strong></p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p><strong>geo_code</strong></p> </td> <td> <p>2011 LSOA/DZ geo-code of origin</p> </td> </tr> <tr> <td> <p><strong>mode</strong></p> </td> <td> <p>Mode of transport used for the indicators, values: 'pt' = public transport, 'bicycle', 'walk'</p> </td> </tr> <tr> <td> <p><strong>time_of_day</strong></p> </td> <td> <p>Time of departure, values: 'am' = 7 a.m., 'pm' = 9 p.m. Available for public transport only.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_15</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 15 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_30</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 30 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_45</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 45 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_60</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 60 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_75</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 75 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_90</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 90 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_105</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 105 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_120</strong></p> </td> <td> <p>Cumulative accessibility: Number of services of type k within 120 minutes</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_15_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 15 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_30_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 30 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_45_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 45 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_60_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 60 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_75_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 75 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_90_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 90 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_105_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 105 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>access_<NAME OF SERVICE>_120_pct</strong></p> </td> <td> <p>Relative cumulative accessibility: Number of services of type k within 120 minutes. In percent, from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>nearest_<NAME OF SERVICE></strong></p> </td> <td> <p>Travel time in minutes to the nearest service of type k</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset or part of it, you are encouraged to use the following citation:</p> <div> <div>1. J Rafael Verduzco Torres, & David McArthur. (2024). Great Britain Accessibility Indicators 2023 (AI23) (Version 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14778265</div> <div>2. Verduzco Torres, J. R., & McArthur, D. P. (2024). Public transport accessibility indicators to urban and regional services in Great Britain. <em>Scientific Data</em>, <em>11</em>(1). <a href="https://doi.org/10.1038/s41597-023-02890-w">https://doi.org/10.1038/s41597-023-02890-w</a></div> <div>3. Verduzco Torres, J. R., & McArthur, D. P. (2024a). <em>Great Britain Accessibility Indicators 2023: Data descriptor</em>. OSF. <a href="https://doi.org/10.31235/osf.io/qb9j4">https://doi.org/10.31235/osf.io/qb9j4</a></div> </div> <pre><code><br>@article{VerduzcoTorres2024,<br> title = {Public Transport Accessibility Indicators to Urban and Regional Services in {{Great Britain}}},<br> author = {Verduzco Torres, J. Rafael and McArthur, David Philip},<br> year = {2024},<br> month = jan,<br> journal = {Scientific Data},<br> volume = {11},<br> number = {1},<br> pages = {53},<br> publisher = {{Nature Publishing Group}},<br> doi = {10.1038/s41597-023-02890-w},<br> urldate = {2024-01-15},<br> }<br> @article{VerduzcoTorres2024a,<br> title = {Great {{Britain Accessibility Indicators}} 2023: {{Data}} Descriptor},<br> shorttitle = {Great {{Britain Accessibility Indicators}} 2023},<br> author = {Verduzco Torres, Jose Rafael and McArthur, David Philip},<br> date = {2024-03-23},<br> eprinttype = {OSF},<br> doi = {10.31235/osf.io/qb9j4},<br> url = {https://osf.io/qb9j4},<br> urldate = {2024-03-25}<br>} </code></pre> <p> </p> <p> </p>
Accessibility Rank: A Machine Learning Approach for Prioritising Accessibility User Feedback
<p>This repository serves as a comprehensive collection of datasets, code scripts, and associated data used in my master's research conducted at the University of Auckland on accessibility-related reviews. The research findings and methodology are described in detail in our paper titled "Accessibility Rank: A Machine Learning Approach for Prioritising Accessibility User Feedback". By making these resources openly available, we aim to foster collaboration, reproducibility, and advancement in the field of accessibility research. Researchers and developers can leverage these datasets, associated data, and code scripts to gain insights, validate findings, and explore novel approaches to addressing accessibility challenges.</p> <p>We encourage users to refer to our paper for a comprehensive understanding of our research methodology, experimental setup, and results. Proper attribution and citation of our paper are appreciated when utilizing any part of this repository in further research or publications.</p>
Accession passport and sequence data
<p>Accession passport data corresponding to <em>An2-like</em> and <em>Ant1 </em>sequence data.</p>
Data from: Pitfalls and pointers: an accessible guide to marker gene amplicon sequencing in ecological applications
<p>Next Generation Sequencing (NGS) is a powerful tool that has been rapidly adopted by many ecologists studying microbial communities. Despite the exciting demonstration of NGS technology as a tool for ecological research, cryptic pitfalls inherent to its use can obscure correct interpretation of NGS data. Here, we provide an accessible overview of a NGS process that uses marker gene amplicon sequences (MGAS) that will allow scientists, particularly community ecologists, to make appropriate methodological choices and understand limits on inference about community composition and diversity that can be drawn from MGAS data.</p> <p>We describe the MGAS pipeline, focusing specifically on cryptic sources of variation that have received less emphasis in the ecological literature, but which may substantially impact inference about microbial community diversity and composition. By simulating communities from published microbiome data, we demonstrate how these sources of variation can generate inaccurate or misleading patterns.</p> <p>We specifically highlight sample dilution without researcher awareness and lane-to-lane variability, two cryptic sources of variation arising during the MGAS pipeline. These sources of variation affect estimates of species presence and relative abundance, particularly for species with moderate to low abundances. Each of these sources of bias can lead to errors in the estimation of both absolute and relative abundance within, and turnover among, microbial communities.</p> <p>Awareness and understanding of what happens and, specifically, why it happens during MGAS generation is key to generating a strong data set and building a robust community matrix. Requesting sample dilution information from the sequencing center, including technical replicates across sequencing lanes, and understanding how sampling intensity and community taxa distribution patterns shape the measurement of community richness, evenness, and diversity are critical for drawing correct ecological inferences using MGAS data.</p>
Vebinar - Open Access
<p>The Faculty of Agriculture in Novi Sad, for the needs of the implementation of project activities within the CO-Change H2020, organized a webinar on Open Access, which was held on February 10, 2022 through the application ZOOM. The following webinar topics were covered:</p> <p>Green OA, PID (DOI, handle) for datasets, open data, researcher responsibilities, librarian responsibilities.</p> <p><br> Lecturers:</p> <p>Milica Ševkušić, librarian at the Institute of Technical Sciences of SANU and administrator of the institutional repository. He is part of the Repository Development Team of the Computer Center of the University of Belgrade. Participates in the realization of educational programs of the Section of Librarians and Librarians of the Association of Institutes of Serbia and the Association of Open Science of Serbia. Since 2014, he has been the national coordinator for open access at the international consortium EIFL.</p> <p>Ljiljana Radisavljević, graduate librarian-computer scientist at the Institute of Vegetables. He runs the scientific research library at the institute and is the administrator of the institutional repository. He leads training programs for researchers employed at the Institute in the field of communication systems in science, use of sources of scientific information, open access, open science, copyright and evaluation of scientific work, support for the implementation of open science policies.</p> <p>Support</p> <p>Biljana Kosanović, Computer Center of the University of Belgrade - RCUB.</p> <p>Representative of PFNS and Co-Change</p> <p><br> Dejan Beuković, Assistant Professor at the Faculty of Agriculture, University of Novi Sad.</p>
Supplementary Data - Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions
<p><strong>File: 50K_GenotypesEU_raw_UHOH_SoySNP50K.csv.tgz </strong></p> <p>Genotyping data of SoySNP50k SNP array of 170 European soybean varieties.</p> <p>The array includes 51.955 SNP markers.</p> <p>Genotypes of each variety are in columns and each row is a SNP marker. Naming of markers follows the annotation of the soybean genome.</p> <p><strong>File: EUvarieties_infos.csv </strong></p> <p>Description of European varieties</p> <p>Contains variety name, country of origin, EU region and maturity group assignment.</p> <p> </p> <p><strong>File: Supplementary_Data_Haupt_Schmid.xlsx</strong></p> <p>Additional data derived from data analysis. Description of data contained within file (Worksheet "Summary")</p> <p> </p>
Open access data from the International Design Engineering Annual (IDEA) Challenge 2021
<p>Open access dataset from the IDEA challenge 2021. </p> <p>The generation of this dataset has been undertaken as part of the ProtoTwin project (Improving the product development process through integrated revision control and twinning of digital-physical models during prototyping). The work was conducted at the University of Bristol in the Design and Manufacturing Futures Lab (<a href="http://www.dmf-lab.co.uk/">http://www.dmf-lab.co.uk</a>) and is funded by the Engineering and Physical Sciences Research Council (EPSRC), Grant reference <a href="https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R032696/1">EP/R032696/1</a>. The dataset was generated in collaboration with the Norwegian Technical University (NTNU), University of Zagreb and University of Twente.</p> <p>For more information please contact Mark (mark.goudswaard @ bristol.ac.uk) or James ( james.gopsill @ bristol.ac.uk)</p>
A survey of access to the digital collections of 195 UK GLAMs across internal and external platforms - Appendix 1 for A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK
<p>Created for the<a href="https://doi.org/10.5281/zenodo.6242611"> 'A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK' </a>report, this sample replicates and expands the Open galleries, libraries, archives and museums (GLAMs) Survey data extraction and methodology to include a range of GLAMs across the UK and new data points. The initial sample of 350 organisations included Independent Research Organisations (IROs) and Research Centre Institutes (RCIs), GLAMs associated with <a href="https://www.nationalcollection.org.uk/">Towards a National Collection</a> Foundation and Discovery projects, UK GLAMs in the Open GLAM Survey, and other UK GLAMs and related organisations. An initial review was performed to identify and remove organisations outside the scope of inquiry (<em>e.g.</em>, no permanent collections). The final sample included 195 organisations.</p> <p>From the final sample, 24 are IROs (all RCIs were removed). Another 32 are Universities (including GLAMs within universities). This brings the total number of organisations eligible for AHRC funding to 56 (or 28.6%). The remaining 140 include public and private GLAMs at national, regional and local levels (<em>e.g., </em>councils, historic buildings) and research initiatives or data aggregators (<em>e.g.,</em> Portable Antiquities Scheme, Culture Grid, Archaeology Data Service). Organisations are distributed across the UK as follows: Channel Islands (1 total); England (154 total); Isle of Man (1 total); Northern Ireland (5 total); Scotland (28 total); Wales (6 total).</p> <p>A survey of the copyright and open access policies of 63 GLAMs from the UK GLAM Sample is available on Zenodo at: <a href="https://doi.org/10.5281/zenodo.6242559">https://doi.org/10.5281/zenodo.6242559</a></p>
Equitable Access to Residential (EQUATOR) EV Charging: Optimal Investments in EV charging
<p>This folder comprises Julia codes produced to determine optimal investments in EV charging for Equitable Access to Residential (EQUATOR) EV Charging project. The file descriptions are below:</p> <p> </p> <p>1. Generator.csv: contains generator data for all the generators in the Manhattan power network</p> <p>2. Node.csv: contains the load data for each bus in the Manhattan power network</p> <p>3. Line.csv: contains technical line parameters for all the lines in the Manhattan power network</p> <p>4. NetworkDataType.jl and NetworkLoad.jl: Julia files to process csv data and design the Manhattan power grid</p> <p>5. Justice_LL.jl: Justice modeling of the power utility</p> <p>6. Justice_Case1_fixed.jl: Julia file for determining optimal investments in EV charging in Manhattan.</p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Planning universal accessibility to public healthcare in sub-Saharan Africa
<p>Replication code and data for the the paper "Planning universal accessibility to public healthcare in sub-Saharan Africa". </p> <p>The tarball contains computer code (in R and javascript) and input data to replicate or update the analysis and the figures, and the result data of baseline and sensitivity analysis model runs. Powerful (or cloud, e.g. Google Earth Engine, RStudio Cloud, or Google Colab) computing facilities are recommended for a successful replication.</p>
Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda":</p> <p><strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country.</p> <p><strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).</p>
How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data carpentry (dataset)
<p>This data set is related to the book chapter with the following bibliographic details - Mukhopadhyay, P. (2022). How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data<br> carpentry. In A. Biswas & M. Das Biswas (Eds.), Panorama of open access: Progress, practices & prospects (1st ed., pp. 67–89). Ess Ess. https://doi.org/10.5281/zenodo.6511080.</p> <p>It includes the truncated version of the final data set that has been used for analyzing Open Access Friendliness (OAF) of the Indian Institutes of Technology (IITs). The zipped version of the data set is around 95 MB (465 MB after decompress).</p>
Number of imperial portraits (average), per year of reign (N=1625), excluding emperors that ruled less than a year and imperial portraits that circulated prior to the emperor's accession
<p>The figure presented here includes the average number of sculptural portraits of Roman emperors (mostly carved from marble or casted in bronze), per year of rule, that were collected for the purposes of analyzing the representation of Roman emperors in freestanding sculpture. PhD dissertation: S. Heijnen (2022), Portraying Change: The Representation of Roman Emperors in Freestanding Sculpture (ca. 50 BC - ca. 400 AD). Dissertation. Radboud University.</p>
Acknowledged scholars extracted from open access journals
<p><strong>Published paper</strong><br> For details of the data development: </p> <p>Kusumegi, K., Sano, Y. Dataset of identified scholars mentioned in acknowledgement statements. Sci Data 9, 461 (2022). https://doi.org/10.1038/s41597-022-01585-y</p> <p>To use the data, please cite the above mentioned paper .</p> <p> </p> <p><strong>Data on scholars mentioned in acknowledgments extracted from open access journals.</strong></p> <p>This data firstly updated the repository at University of Tsukuba (Division of Policy and Planning Sciences Commons).</p> <p>https://commons.sk.tsukuba.ac.jp/data_en</p> <pre> <strong>Each csv correspond to:</strong> biology.csv as PLOS Biology compbiology.csv as PLOS Computational Biology genetics.csv as PLOS Genetics medicine.csv as PLOS Medicine ntds.csv as PLOS Neglected Tropical Diseases pathogenes.csv as PLOS Pathogens plosone.csv as PLOS ONE srep.csv as Scientific Reports Data details: Doi: DOI of the paper PaperId: Paper ID in MAG AcknowledgedId: Acknowledged scholar's ID in MAG CollaborationApproach: Acknowledged scholar is identified by collaboration relationship CitationApproach: Acknowledged scholar is identified by citation relationship </pre> <p> </p>
MOSBRI survey - Biophysical Data Standards and Accessibility
<p>The needs for data standards and formats in molecular biophysics were analysed mainly via a survey focused on data producers and users in the field: Biophysical Data Standards and Accessibility. The questions were focused on identifying the expertise and scientific interests of the respondents, their use of techniques of molecular biophysics, views on the current situation and needs in data formats standardisation and needs for repositories or databases. The data were collected using LimeSurvey technology. Anonymized raw dat, their processing and interpretation included in this dataset. The work was performed as part of the project MOlecular Scale Biophysics Research Infrastructure (MOSBRI).</p> <p> </p>
Ecological data for: Subsidy accessibility drives asymmetric food web responses
<p>Global change is fundamentally altering flows of natural and anthropogenic subsidies across space and time. After a pointed call for research on subsidies in the 1990s, an industry of empirical work has documented the ubiquitous role subsidies play in ecosystem structure, stability and function. Here, we argue that physical constraints (e.g., water temperature) and species traits can govern a species' accessibility to resource subsidies, which has been largely overlooked in the subsidy literature. We examined the input of a high quality, point-source anthropogenic subsidy (aquaculture feed) into a recipient freshwater lake food web. By using a combined bio-tracer approach, we detect a gradient in accessibility of the anthropogenic subsidy within the surrounding food web driven by the thermal preferences of three constituent species, effectively rewiring the recipient lake food web. Since aquaculture is predicted to increase significantly in coming decades to support growing human populations, and global change is altering temperature regimes, then this form of food web alteration may be expected to occur frequently. We argue that subsidy accessibility is a key characteristic of recipient food web interactions that must be considered when trying to understand the impacts of subsidies on ecosystem stability and function under continued global change.</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.