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1,952 results for “Action”
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
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Dataset - Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics
<h4><strong>Dataset Summary</strong></h4> <p>Lysine deacetylase inhibitors (KDACis) are approved for cutaneous T-cell lymphoma (CTCL), peripheral T-cell lymphoma (PTCL), and multiple myeloma. Despite the mechanism of action(s) (MoA) remains elusive, these inhibitors lead to increasing acetylation levels of histones and other proteins, altered gene expression and cell death. To characterize the MoA of these drugs in more detail, we systematically measured dose-dependent changes in protein expression, acetylation, and phosphorylation in response to 21 clinical and pre-clinical KDACis. MV4-11 cells were treated for 6 h with 1 vehicle control and 10 increasing doses of the respective drug (from 100 pM to 30 mM). Proteins were digested with trypsin, and the resulting 11 peptide preparations corresponding to one drug dose each were encoded by stable isotopes (tandem mass tags, TMT-11plex) and combined. Acetylated peptides were subsequently enriched by immunoprecipitation and phosphopeptides by immobilized metal affinity chromatography (IMAC). PTM-carrying and unmodified peptides were analyzed separately by liquid chromatography tandem mass spectrometry (LC-MS/MS) for peptide and protein identification and quantification. Additionally, Vorinostat and Panobinostat were also recorded as time-dependent experiments at their pEC50 concentration, respectively. </p> <h4><strong>Dataset structure</strong></h4> <p>Here, we provide all curve data processed with CurveCurator v0.4.0 (<a href="https://github.com/kusterlab/curve_curator">https://github.com/kusterlab/curve_curator</a>). Each drug is a zip folder containing acetylome, phosphoproteome, and fullproteome data. Next to each data set is the toml parameter file used to generate the curves.txt and dashboard.html files. Time-dependent data is indicated by "td" and dose-dependent data is indicated by "dd".</p> <p> </p>
Mappings for "Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach"
<p>Studies identified false and non-actionnable alarms as a factor for alarm fatigue in intensive care units.</p> <p>To annotate patient alarms, and analyse the alarm situation in intensive care units, we conceptualized and performed data mappings related to airway management and medication interventions. The mappings were based on information retrieved from the patient data management system (PDMS) and clinical expertise. For the airway management mappings, we used additional resources such as ISO 19223:2019 or ventilator instruction manuals. The mappings do not include patient data.</p> <p>As the mappings are generic, they could be used in other contexts than alarm annotation and research.</p> <p><strong>1. Respiratory Management Mappings:</strong></p> <ul> <li>General tables summarizing the 1) categories based on ISO 19223:2019 to describe respiratory support therapies (RSTs), 2) defining the invasiveness level of a RST and 3) listing the abbreviations used in the mappings</li> <li> <p>Tables including PDMS entries for airway devices (ADs), ventilation devices (VDs), and ventilation modes (VMs)</p> </li> <li> <p>Mapping of AD entries (from the PDMS) to defined categories</p> </li> <li> <p>Mapping of VDs, VMs, and ADs to defined RSTs, including information on invasiveness</p> </li> <li> <p>Table specifying suitable ventilation parameters in the context of each RST</p> </li> </ul> <p><strong>2. Medication Mappings:</strong></p> <ul> <li> <p>General tables providing information on physiological alarm conditions (PACs), interventions, routes, and techniques of administration of interest</p> </li> <li> <p>Mapping of routes of administration to techniques of administration including PDMS entries</p> </li> <li> <p>Mapping of active ingredients (including SNOMED CT Fully Specified Names and Identifiers), related PDMS information, and routes and techniques of administration to defined PAC and interventions</p> </li> </ul>
Interagency Ecological Program: Water quality, fish, and zooplankton monitoring and modeling to support the 2018 Suisun Marsh Salinity Control Gates Summer Action
In summer 2018 we used a unique water control structure in the San Francisco Estuary (SFE) to direct a managed flow pulse into Suisun Marsh, one of the largest contiguous tidal marshes on the west coast of the United States. The action was designed to increase habitat suitability for the endangered Delta Smelt Hypomesus transpacificus, a small osmerid fish endemic to the upper SFE. The approach was to operate the Suisun Marsh Salinity Control Gates (SMSCG) in conjunction with increased Sacramento River tributary inflow to direct an estimated 160 x 10^6 m3 pulse of low salinity water into Suisun Marsh during August, a critical time period for juvenile Delta Smelt rearing. This dataset includes physical and biological monitoring data collected for the action. Datasets include Delta Smelt catch from the USFWS Enhanced Delta Smelt Monitoring program, zooplankton and Microcystis abundance from the Environmental Monitoring Program, historic Delta Smelt catch from the Summer Townet Survey, Delta Outflow from the Dayflow model, extent of Delta Smelt habitat from the UnTRIM Bay-Delta model, and water quality (Salinity, Temperature, Chlorophyll, and Turibidity) collected at continuous sondes at three locations. These data are associated with the manuscript "Evaluation of a large-scale flow manipulation to the upper San Francisco Estuary: Response of habitat conditions for an endangered native fish," by Dr. Ted Sommer, et al. 2020 PLOS One, in review.
Hippocampal-neocortical interactions sharpen over time for predictive actions
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Action-related object pairs - fMRI dataset
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Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment
<p>Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of <em>in vitro</em> and <em>in silico</em> approaches for chemical testing and assessment.</p> <p> </p> <p> </p>
VIOLENDINGS Violent actions contained in pastoral novels written in Spanish (1559-1633)
<p>This dataset contains a categorization of the violent actions contained in pastoral novels (and in the courtly novels narrated by their characters) written in Spanish between 1559 and 1633 for a diachronic study of the representation of violence in this literary genre and its intersection with other literary traditions. It classifies violent actions by gender and social position of victims and aggressors, relationship between them, motive of aggression, weapon and correspondence with the motives of the Sith Thompson index. In addition, it proposes a categorization for the types of solutions to violent scenes in this literary tradition and the 'distancing devices' used in their representation. The concepts proposed for this categorization are explained in the document INTRO[VIOLENDINGS]20240613_v1. This is the dataset of the research project identified by the acronym VIOLENDINGS —Violence and Happy Endings in the Spanish Golden Age Narrative— (Grant Agreement ID: 101062513),funded by the European Commission’s Marie Skłodowska Curie Actions under Horizon Europe (2021). The project was developed at the Dipartimento di Lingue, Letterature, Culture e Mediazioni of the Università degli Studi di Milano between 2022 and 2024. (2024-06-13) </p> <p> </p> <p> </p>
Interagency Ecological Program: Monitoring of water quality, phytoplankton, zooplankton, clams, and Delta Smelt to support the Summer-Fall Suisun Marsh Salinity Control Gates Action 2018-2024
The Suisun Marsh Salinity Control Gates (SMSCG) have the potential to increase low-salinity-zone habitat for endangered Delta Smelt (Hypomesus transpacificus, California Endangered Species Act listed as Endangered, Federal Endangered Species Act listed as Threatened), and to allow them to more frequently occupy Suisun Marsh, especially Montezuma Slough, one of their most important rearing habitats. Operation of the SMSCG in summer and fall to improve Delta Smelt habitat are called for in the Biological Opinion and Incidental Take permit for the Central Valley Project and State Water Project. To support the adaptive management of the action, the California Department of Water Resources and collaborating agencies monitored water quality, phytoplankton, zooplankton, clams, and fishes during the SMSCG management actions in 2018. Monitoring has continued during the summer and fall months of all subsequent years, including both those with and without actions. This data package includes data collected by the Interagency Ecological Program’s (IEP) long-term monitoring programs supplemented with targeted sample collection where existing surveys lacked spatial or temporal coverage. Monitoring during no-action years will be used as a baseline for comparison during action years. This data package will be updated annually.
action-in-auctions
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Cyclist Actions: Optical Flow Sequences and Trajectories
<p>The dataset consists of over 1.1 million samples of labeled cyclists actions. Every sample consists of two optical flow sequences, recorded over the past second (9 optical flow images each), from two different cameras, the past trajectory of the cyclist of the last second (50 past positions), and a label of the currently performed action.</p> <p>The samples were extracted from 1,639 video sequences of cyclists moving across an urban intersection at the University of Applied sciences in Aschaffenburg: <a href="https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/">https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/</a></p> <p>The uploaded files consist of an archive containing 27 numpy files, a single numpy file containing trajectories only, and a json file containing 5-fold cross validation/test split.</p> <p>The numpy files consist of python dictionaries with scenes of the form:</p> <pre><code class="language-python">{SCENE_NAME: 'of_hk1/2': [...], # zip compressed, python pickled optical flow sequences of cameras 1/2 'x/y/z_tracked': [...], # tracked cyclists positions in x/y/z directions, 'x/y/z_smoothed': [...], # smoothed (by rts smoother) cyclists positions in x/y/z directions, 'orientation': [...], # orientation of the cyclists estimated by kalman filters 'ts': [...], # utc timestamps in micro seconds LABEL_NAME: [...], # labels of different actions (0 or 1)}</code></pre> <p>The manually created labels are:</p> <ul> <li>straight: cyclists is moving and not turning</li> <li>tr/tl: cyclist is turning left/right</li> <li>move: cyclist is moving with nearly constant velocity and not turning</li> <li>start: cyclist was standing and starts moving</li> <li>starting_movement: first movement of cyclist before starting</li> <li>stop: cyclist was moving/starting and slows down to a halt</li> <li>wait: cyclist is standing</li> <li>hand_signal_left/right: cyclist indicates a turn by hand signal</li> <li>shoulder_check_left/right: cyclist looks over left/right shoulder</li> <li>out_of_saddle: cyclist is standing</li> </ul> <p>The optical flow sequences were created using PWC-Net [1].</p> <p>To extract the zipped/pickled optical flow sequences:</p> <pre><code class="language-python">import cv2 as cv import zlib import pickle import numpy as np # visualize flow def vis_of(of): hsv = np.zeros([of.shape[0], of.shape[1], 3], dtype=np.uint8) hsv[..., 1] = 255 mag, ang = cv.cartToPolar(of[..., 0].astype(np.float32), of[..., 1].astype(np.float32)) hsv[..., 0] = ang * 180 / np.pi / 2 hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX) bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR) return bgr # load npy file from dataset npy_path = 'of_dataset_0.npy' data = np.load(npy_path, allow_pickle=True).item() scene = data[list(data.keys())[0]] # extract optical flow sequence ofs = pickle.loads(zlib.decompress(scene['of_hk1'][i])).astype(np.float16) * 2.0 / 255.0 - 1.0 # show of images in sequence for j in range(len(ofs)): # create bgr image from 2 channel optical flow bgr = vis_of(ofs[j]) cv.imshow("of", bgr) </code></pre> <p>Python code and a description to read the dataset can be found in our GitHub: <a href="https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition">https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition</a></p> <p>[1] D. Sun, X. Yang, M. Liu, and J. Kautz, “PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018, pp. 8934–8943.</p> <p> </p> <p>This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern".</p> <p>Due to privacy laws in germany, we are not permitted to publish image sequences.</p>
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
<p>This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More information can be found in the README that is also included.</p>
Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring
<p>There are two files for each subject:</p> <p>1. sub##_error.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. sub##_hit.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 7 cm) in the task (segment and electrode information can be found below).</p> <p><br> The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodes</p> <p>n represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726 (850 ms after ball release) in every segment.</p> <p>datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,<br> i.e. hit/error #1 -> row 1 to 1400, hit/error #2 -> row 1401 to 2800, ..., hit/error #n -> (n-1) * 1400 + 1 to n * 1400</p> <p>electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]</p>
Dataset: Temporal recalibration in response to delayed visual feedback of active versus passive actions
<p>Data set related to the manuscript: </p><p>Kufer, K., Schmitter, C. V, Kircher, T., Straube, B., 2023. Temporal recalibration in response to delayed visual feedback of active versus passive actions: An fMRI study. https://doi.org/10.21203/RS.3.RS-3493865/V1</p><p>Abstract:</p><p>The brain can adapt its expectations about the relative timing of actions and their sensory outcomes in a process known as temporal recalibration. This might occur as the recalibration of timing between the outcome and (1) the motor act (sensorimotor) or (2) tactile/proprioceptive information (inter-sensory). This fMRI recalibration study investigated sensorimotor contributions to temporal recalibration by comparing active and passive conditions. Subjects were repeatedly exposed to delayed (150ms) or undelayed visual stimuli, triggered by active or passive button presses. Recalibration effects were tested in delay detection tasks, including visual and auditory outcomes. We showed that both modalities were affected by visual recalibration. However, an active advantage was observed only in visual conditions. Recalibration was generally associated with the left cerebellum (lobules IV, V and vermis) while action related activation (active > passive) occurred in the right middle/superior frontal gyrus during adaptation and test phases. Recalibration transferred from vision to audition was related to action specic activations in the cingulate cortex, the angular gyrus and left inferior frontal gyrus. Our data provide new insights in sensorimotor contributions to temporal recalibration via the superior frontal gyrus and inter-sensory contributions mediated by the cerebellum.</p>
HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction
<p>A thorough analysis of the existing human action recognition datasets demonstrates that only a few HRI datasets are available that target real-world applications, all of which are adapted to home settings. Therefore, given the shortage of datasets in industrial tasks, we aim to provide the community with a dataset created in a laboratory setting that includes actions commonly performed within manufacturing and service industries. In addition, the proposed dataset meets the requirements of deep learning algorithms for the development of intelligent learning models for action recognition and imitation in HRI applications.</p>
Evaluation of surface properties that influence the self-cleaning action of hydrophobic plant leaves
<p>It is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Twelve different leaf types were selected for analysis due to their water repellency and self-cleaning properties.</p>
Annex 1 – Actions and measures relevant to research integrity matched to the UK Concordat
<p>The present dataset is an Annex to the Discussion Document entitled “<a href="https://doi.org/10.5281/zenodo.6827947">Indicators of Research Integrity: An initial exploration of the landscape, opportunities and challenges</a>”. </p> <p>It consists in a longlist of actions and measures that organisations may put in place to support research integrity, building on a set of documents that we considered to represent the perspectives of the stakeholder groups mentioned in the UK Concordat to Support Research Integrity, including: </p> <ul> <li> <p>researchers; </p> </li> <li> <p>employers of researchers (i.e. bodies that conduct or host research; employ, support or host researchers; teach research students; or allow research to be carried out under their auspices); </p> </li> <li> <p>research funders; and </p> </li> <li> <p>other organisations (e.g. professional, statutory and regulatory bodies; academies and learned societies; professional and subject-specific representative bodies; journals and publishers; and organisations offering advice, guidance and support). </p> </li> </ul> <p>The table below provides an overview of the documents covered in the dataset. It should be noted that our selection of documents is not meant to imply that other efforts are of lesser importance: it is only a starting point for discussion and seeks to represent a breadth of stakeholder views. </p> <table> <tbody> <tr> <td> <p>Document </p> </td> <td> <p>Lead </p> </td> <td> <p>Main perspective(s) </p> </td> </tr> <tr> <td> <p><a href="https://ukrio.org/wp-content/uploads/UKRIO-Self-Assessment-Tool-for-The-Concordat-to-Support-Research-Integrity-V2.pdf">UKRIO Self-Assessment Tool for The Concordat to Support Research Integrity</a> </p> </td> <td> <p>UK Research Integrity Office (UKRIO) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.1371/journal.pbio.3000737">The Hong Kong Principles for assessing researchers: Fostering research integrity</a> </p> </td> <td> <p>Moher et al. (academic article) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.vitae.ac.uk/vitae-publications/reports/research-integrity-a-landscape-study">Research integrity: a landscape study</a> </p> </td> <td> <p>UK Research and Innovation (UKRI), Vitae, UK Research Integrity Office (UKRIO), UK Reproducibility Network (UKRN) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="https://wellcome.org/reports/what-researchers-think-about-research-culture">What Researchers Think About the Culture They Work In</a> </p> </td> <td> <p>Wellcome </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.allea.org/wp-content/uploads/2017/05/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017.pdf">The European Code of Conduct for Research Integrity</a> </p> </td> <td> <p>All European Academies (ALLEA) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="http://www.enrio.eu/wp-content/uploads/2019/03/INV-Handbook_ENRIO_web_final.pdf">Handbook on Research Integrity</a> </p> </td> <td> <p>European Network for Research Ethics and Integrity (ENERI) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://sops4ri.eu/wp-content/uploads/Guideline-for-Promoting-RI-in-RFOs_final.pdf">Guideline for Promoting Research Integrity in Research Funding Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Research funders </p> </td> </tr> <tr> <td> <p><a href="https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/guideline-for-promoting-research-integrity-in-research-performing-organisations_horizon_en.pdf">Guideline for Promoting Research Integrity in Research Performing Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2018.1.3">Cooperation between research institutions and journals on research integrity cases: guidance from the Committee on Publication Ethics</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers and Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2019.1.4">COPE Retraction Guidelines</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers </p> </td> </tr> </tbody> </table> <p>Find more outputs of this project in the <a href="https://zenodo.org/communities/research-integrity-indicators/">dedicated Zenodo community</a>. </p>
Sample Records: Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>The project contains several underlying datasets essential for replicating the study's findings. The dataset <strong>01.1_PRIMERPRISMA_IDENTIFICATION.xlsx</strong> includes the initial selection of 6 general terms related to environment and sustainability and 11 specific terms related to disinformation, summarizing the selected keywords, generated Boolean operators, and initial search results, yielding 783 records. The <strong>01.2_PRIMER PRIMA-SCREENING.xlsx</strong> file details the screening process, eliminating duplicates and non-English documents, resulting in 271 retained records. The <strong>01.3_PRIMER PRISMA_INCLUDED.xlsx</strong> file contains results after further screening, retaining 82 documents with expanded bibliometric details. The <strong>02.1_SEGUNDOPRISMA_IDENTIFICATION.xlsx</strong> file documents the second phase of identification using new terms related to climate and disinformation, retrieving 174 records. The <strong>02.2_SEGUNDOPRISMA_SCREENING.xlsx</strong> file includes the screening process for the second phase, reducing records to 75, with an abstract review retaining 2 documents. The <strong>02.3_SEGUNDOPRISMA_INCLUDED.xlsx</strong> file integrates documents from both search phases and other sources, culminating in a final review of 86 documents. The <strong>3.1_Other sources.xlsx</strong> file includes additional relevant sources identified during the review process. Finally, the <strong>4-Final included.xlsx</strong> file contains the final set of 75 publications subjected to the DESLOCIS analysis model.</p>
Sample Records (Analytical procedure): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes t<span>he online form and the results from the quantitative phase of the study: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon</span></p> <p>To duplicate the form you can use: https://forms.office.com/Pages/ShareFormPage.aspx?id=6sSEXw03nkuDDHVvi_G1Hw0s3dVrMb1NsO12gDNTB9BUREo4WENRMFFDN1lOSlRSU0xJNkVHWURWUS4u&sharetoken=rg4Qfg19O4UgYzUB084C </p>
Sample Records (PRISMA Checklist and Flow diagram): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes: the PRISMA Checklist and the <span>PRISMA Flow diagram of the study titled: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon.</span></p>
ScienceDex guides
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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.