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9,882 results for “Scale”
Supplement to "Hydro-Meteorological Drivers of Event Runoff Characteristics Under Analogous Soil Moisture Patterns in Three Small-Scale Headwater Catchments"
<p>The dataset is a supplement to the manuscript "Hydro-Meteorological Drivers of Event Runoff Characteristics Under Analogous Soil Moisture Patterns in Three Small-Scale Headwater Catchments" and contains the processed time series data of the Wüstebach, Rollesbroich, and Petzenkirchen catchments. Hydro-metorological variables include precipitation, runoff, soil moisture, and groundwater level.</p>
Data Supporting Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model
Open the record for dataset details and reuse information.
Europe SSP air quality PM2.5 (VD Scaled) O3 mortality output
Open the record for dataset details and reuse information.
Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex
<p># Data and srcipts used to perform analyses presented in the manuscript: "Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex"</p>
Pilot scale on-site demonstration and seasonality assessment of nitrogen recovery and water reclamation from pig's slurry liquid fraction
Open the record for dataset details and reuse information.
Gridded maps of global population scaled to match the 2023 Wittgenstein Center (WIC) Population projections
<p>The gridded population data used for calculating exposed populations is based on the population projections from the original SSPs (KC and Lutz, 2017) which were subsequently gridded (Jones and O’Neill, 2016). These gridded projections were aggregated to 0.5 ° spatial resolution and then scaled to match the latest available projections for population in line with the updated SSPs, v3.0 (KC <em>et al.</em>, 2024). The scaling is done on a country basis for all countries included in the latest SSP projections. Countries, which are not included in these projections, remain unchanged. The scaling process is done on a country-level basis using the following step:<br> </p> <ol> <li>The total population for the original gridded data is calculated using the ISIMIP fractional country raster (Perrette, 2023), excluding border cells containing contributions from more than one country.</li> <li>The population of the fractional border cells is subtracted from the total population of the SSP population projections and the required scalar to match the population from the gridded data to the SSP population projections is calculated.</li> <li>This scalar is applied to all non-fractional cells. </li> </ol> <p>While it is possible to calculate the scalar for each country including the proportion of the population in the fractional border cells, this would require the scalar to also be applied to that proportion of the population in the border cells to match the overall population number for each country. Applying different scalars to the population proportions for each country in the same cell would, however, change the ratios of the population in the fractional border cells and subsequently lead to skewed results when reapplying the fractional country raster to the scaled data for the aggregation to country level. For small countries, where more population lives in fractional border cells than in non-border cells, and for countries that only consist of border cells with contributions from more than one country, all cells were used in the scaling process. <br><br>It should be noted that some smaller countries cannot be scaled properly and that the latest SSP population projections do not contain values for all countries. Since there has been no release of updated gridded population projections yet, the gridded population data created using this approach still provide the closest match to the latest SSP population projections currently available.</p>
Data for: Measuring receptivity to misinformation at scale on a social media platform
<p><strong>General Information</strong></p> <p>This contains the data for the publication:</p> <blockquote> <p>Tokita CK, Aslett K, Godel WP, Sanderson Z, Tucker JA, Nagler J, Persily N, Bonneau RA. (2024). Measuring receptivity to misinformation at scale on a social media platform. <em>PNAS Nexus</em>.</p> </blockquote> <p>Please see the above peer-reviewed article that resulted from this data for more details.</p> <p>Raw and original data are located in the `<em>data/</em>` directory, while data that is generated from intermediate analysis is found in the `<em>data_derived/</em>` directory.</p> <p>Please see the directory in the Github repository <a href="https://github.com/christokita/news-belief-at-scale">https://github.com/christokita/news-belief-at-scale </a>for the code that analyzes this data and generates derived data. The code expects both the `<em>data/</em>` and `<em>data_derived/</em>` folders to reside within the same directory.</p> <p>We also included a README with a description of each directory and subdirectory of the data.</p> <p> </p> <p><strong>Abstract (for main paper)</strong></p> <p>Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation's initial spread. Our paper provides a more precise estimate of misinformation's impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.</p> <p> </p> <p><strong>Significance Statement (for main paper)</strong></p> <p>As social media platforms grapple with misinformation, our study offers a new approach to measure its spread and impact. By combining survey data with social media data, we estimate not only the number of users exposed to false (and true) news but also the number of users likely to believe these news stories. We find that the impact of misinformation is not evenly distributed, with ideologically extreme users being more likely to see and believe false content, often encountering it before others. Our simulations suggest that current interventions may have limited effectiveness in reducing the exposure of receptive users. These findings highlight the need to consider individual user receptiveness when measuring misinformation's impact and developing policies to combat its spread.</p>
Input data and code related to "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe"
<p>Datasets and code for the submitted article: "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe" </p> <p>by Alissa Ganter<sup>1,2</sup>, Tyler H. Ruggles<sup>2</sup>, Paolo Gabrielli<sup>1</sup>, Giovanni Sansavini<sup>1,*</sup>, Ken Caldeira<sup>2</sup></p> <p><sup>1</sup> Institute of Energy and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland</p> <p><sup>2</sup> Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA</p> <p><sup>*</sup> Corresponding author: email - sansavig@ethz.ch</p> <p>All rights lie with the authors. Refer to the README.docx for a description of the datasets and their usage in the article.</p>
Scale invariance in early embryonic development
<p>Data and analysis accompanying the paper:</p> <p>"Scale invariance in early embryonic development" </p> <p>Milos Nikolic, Victoria Antonetti, Feng Liu, Gentian Muhaxheri, Mariela D. Petkova, Martin Scheeler, Eric M. Smith, William Bialek and Thomas Gregor. </p> <p>arxiv.org/abs/2312.17684</p>
FIGURE 3 in A new species of the soft scale insect genus Pulvinaria Targioni Tozzetti (Hemiptera: Coccomorpha: Coccidae) on Rhododendron spp. in Northern Europe
FIGURE 3. Leaves of Rhododendron x yakushimanum 'Percy Wiseman' covered in sooty mould and with some Pulvinaria rhododendri Kahrer & Hodgson, sp. nov. on the twigs. Photograph by Inger-Lise Fonneland.
FIGURE 1 in A new species of the soft scale insect genus Pulvinaria Targioni Tozzetti (Hemiptera: Coccomorpha: Coccidae) on Rhododendron spp. in Northern Europe
FIGURE 1. Young adult females and late-stage nymphs of Pulvinaria rhododendri Kahrer & Hodgson, sp. nov. on Rhododendron repens 'Scarlet Wonder'. Photograph by Inger-Lise Fonneland.
GEOLAB-PEBSTER-TU Darmstadt: Large-scale model test on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<h1><strong>Large-scale model test on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB PEBSTER TU Darmstadt project on Zenodo! This repository contains the measurement data of the large-scale model test on Piled Embankments with a Basal Steel Reinforcement (PEBSTER). Pile-supported embankments with basal geosynthetic reinforcement are commonly built in soft soil areas. Steel mesh reinforcement is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from a large-scale test on steel-reinforced piled embankments, conducted at the Institute of Geotechnics of the Technical University of Darmstadt (Schneider et al., 2024a,b,c). This test is part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes four small-scale test (van Eekelen et al., 2024), conducted at Deltares, Delft, Netherlands.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Deltares, Netherlands, Keller (Germany, France, Poland), and SHM System, Poland.</p>
FIGURE 13. Stegnogramma wilfordii—A. Habit. B. Scales. C. Upper stipe. D. Venation. E. Sori. C.-W. Chen Wade6010 in A newly recorded genus and twelve newly recorded species of ferns for Vietnam from Lang Biang Plateau
FIGURE 13. Stegnogramma wilfordii—A. Habit. B. Scales. C. Upper stipe. D. Venation. E. Sori. C.-W. Chen Wade6010.
FIGURE 12. Pteris latipinna—A. Habit. B. Rhizome and stipe scales. C. Basal Lamina. D. Sori. E. Adaxial pinna. F. Stipe cross section. C.-W. Chen Wade5727 in A newly recorded genus and twelve newly recorded species of ferns for Vietnam from Lang Biang Plateau
FIGURE 12. Pteris latipinna—A. Habit. B. Rhizome and stipe scales. C. Basal Lamina. D. Sori. E. Adaxial pinna. F. Stipe cross section. C.-W. Chen Wade5727.
FIGURE 11. Polystichum prionolepis—A. Habit. B. Stipe scales. C in A newly recorded genus and twelve newly recorded species of ferns for Vietnam from Lang Biang Plateau
FIGURE 11. Polystichum prionolepis—A. Habit. B. Stipe scales. C. Bud on distal lamina. D. Adaxial pinnae. E. Sori. C.-W. Chen Wade5108.
FIGURE 8. Dryopteris hendersonii—A. Habit. B. Rhizome. C. Stipe scales. D in A newly recorded genus and twelve newly recorded species of ferns for Vietnam from Lang Biang Plateau
FIGURE 8. Dryopteris hendersonii—A. Habit. B. Rhizome. C. Stipe scales. D. Adaxial side of pinnae. E. Sori. F. Venation (backlight). C.-W. Chen Wade5094.
Knowledge needed for large-scale landscape restoration projects: scientific and experiential. 13.2B Session, Society for Ecological Restoration Conference 2024, Room Hurt, 16:30 -18:00, Thursday, 29th August
<p><strong>Disclaimer: </strong>The DOI of this video does not apply to the content of the slides of the contributors' presentations during this session. The authors of each communication maintain their copyright on the content of the presentations' slides.</p> <p><strong>The creators' roles were as follows:</strong> I. V. A., chair, session coordinator, and presenter; B. S., session coordinator; E. P. M., session coordinator, and presenter; H. J., presenter; F. S., presenter; W. E., presenter; G. M., presenter; and P. I., presenter.</p> <p><strong>Session description</strong></p> <p>This session aimed to explore the critical capacities and functions relevant to the large-scale ecosystem restoration (LER) economy and point out the complementarity of scientific and experiential knowledge and the key role of case studies in increasing this resilience. The restoration economy can be defined as “the market consisting of a network of businesses, investors, consumers, and government initiatives engaging in or driving the economic activity related to ecological restoration.” Its segment dealing with LER can be delimited by the large complexity of the system related to the natural, legislative, institutional, funding, project structure, and organizational network to implement the project, as well as of the economic sectors involved in implementing the projects. Success or failure depends on the interplay between the variables describing the environment of the project, its structure, and its functioning. For success, the uptake of scientific information is as important as the experiential (tacit) knowledge of practitioners, stakeholders, and policymakers. Scientific knowledge can be delivered to the LER economy by structures (models) and data (variables and measurements/estimations of their values). Still, in practice, it is often not a limiting resource. Experiential knowledge can be transferred to some extent by narratives (stories, opinions, examples in common language). The role of case studies for LER is to provide hints about what worked and did not work in other situations and, on this basis, to prepare the adaptive management and increase the resilience of each new project. The session provided opportunities for scientists to interact with practitioners about the problem of LER adaptive management and illustrate the issues with examples of scientific knowledge transfer to current or potential projects and case studies. The session was conceived as crosscutting the missions of the LER and European sections of the Society of Ecological Restoration. It is relevant for the development of the restoration economy in the EU through the implementation of the future European Nature Restoration Law, its effectiveness, and its efficiency.</p> <p> </p> <p><strong>Session report</strong></p> <p><em><strong>Three key elements emerged from the session:</strong></em></p> <p>· Even though there is a widespread need for this kind of knowledge in all phases of project and program cycle development, the role of experiential knowledge/ know-how in ecological restoration is not explicitly treated.</p> <p>· The proprietary know-how can be included in portfolios of patents for technologies and services needed in landscape restoration, which are open-access or undisclosed by practitioners. These types are functionally complementary in the restoration industry and appropriate for specific circumstances.</p> <p>· One can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects.</p> <p> </p> <p><em><strong>Narrative of the session:</strong></em></p> <p>The session showed exciting complementarity between various restoration project phases and provided solid arguments about the importance of experiential knowledge [1]. In the first communication, Jeoelen showed us social science research about what factors positively and negatively control cooperation in the restoration ecosystem of individuals, private organizations, NGOs, and public institutions. Then, she provided information about guidelines for practitioners about how to build trustful relations. She was asked what solutions one has when there are people in the team with undesirable traits, and the answer was to avoid such persons from the very beginning or, if one needs that in the team for other qualities, to discuss with them explicitly the problems. The second speaker, Sebastian, contributed with a complex process-based socio-ecological model presentation and its use in decision-making. His question was about the possibility of including details of cost-benefit analysis in the models of stakeholders' decisions, and the answer was that, in principle, it is possible. However, one must also consider the model's optimal complexity in terms of incertitude propagation. In the third presentation, Pal and Erik presented the practical approach to peatland restoration in catchments from Norway. In their context, it seemed feasible because of data available for hydrological modeling from other state monitoring systems and the fact that most restoration projects occurred on public land.</p> <p>Several examples demonstrated the approach's success in a catchment near Oslo, where the public's interest in recreation is high, and there is support for restoration. The question from the audience related their work to the first presentation of the session topic, namely human resources and trust building with stakeholders. In their context, the usual interactions and roundtables with stakeholders before the start of the projects, as well as continuous formal and informal communication in the implementation phase, were enough to build the needed social capital for a successful restoration outcome. In the first online presentation, Martina provided details about non-formal education projects in marine areas that need restoration ecology. The target group was children, but the projects also engaged adults and environmental professionals from the local socio-ecological systems, thus propagating the informed trust building to the whole community. Her details pointed out the experiential approach in education. Iulia from WWF Romania underlined the importance of case studies in communicating restoration's positive and negative aspects to practitioners. She touched on critical points of experiential knowledge related to the genuine national-scale social interest in restoration, despite the significant interest of local users and local authorities and even the existence of funding for restoration. The lack of cooperation between institutions is a limiting factor for up-scaling the restoration to the full Lower Danube River scale. Even in these conditions, WWF-Romania managed to implement many restoration projects on the Danube River and built a solid relationship with the local people by putting an equal accent on nature and people in the design and implementation phase of the projects. Iulia was asked what they do when there are problems with environmental management (such as illegal garbage disposal) that are not formally relevant to the restoration project but occur in the communities they work with. The answer was that they adopted a proactive and solution-oriented approach and started discussions with the people to identify potential solutions and even develop new projects in cooperation that targeted those problems. Finally, Virgil referred to the audience to the content of this supplementary material, as available on the WhatsApp group of the conference in its preliminary form, and communicated about the cross-cutting session 13.0 from Friday as a framework for further discussion of the issues and disentangling opportunities and challenges in landscape restoration. His presentation showed the relationship between scientific knowledge and experiential one (know-how) in the technology readiness level system. It illustrated this notion by developing a TRL6 environmental service for the cumulative impact assessment of multiple active management actions in contaminated river basins, including restoration of buffer zones and control of mining point sources by remediation projects [2].</p> <p> </p> <p><em><strong>Operational concept of experiential knowledge</strong>:</em></p> <p>· Experiential knowledge is undiscursive knowledge needed for practical activities, particularly ecological restoration.</p> <p>· Related terms: know-how, tacit knowledge.</p> <p>· We acquire it by working with experienced people, and the chains of transfer lead to practical traditions.</p> <p>· It can be transferred to some extent by narratives, films, etc, formally communicated by case studies in professional societies, but personal interactions are decisive for the successful transfer.</p> <p>· It complements scientific knowledge, forming the knowledge capital needed for ecological restoration.</p> <p>· It can be proprietary, non-proprietary, disclosed, and undisclosed.</p> <p>· Its availability geographically and in specific phases of the restoration programs and project cycles limits the success of the restoration.</p> <p>· Due to its complexity, large-scale ecosystem/landscape restoration is highly sensitive to the impact of a lack of experiential knowledge.</p> <p>· Experiential knowledge is needed at the first level in all phases of the program and project cycles and in all themes associated with landscape restoration (at general objectives formulation, implementation/monitoring, financing, and supporting the local economies). Appendix 1 of the full report [1] shows a potential distribution of the contributions to landscape restoration sessions and workshops by these themes, referring in several cases to experiential knowledge).</p> <p>· An extra level of experiential knowledge is needed to successfully transfer first-level know-how/ experiential knowledge.</p> <p>· Despite its unstructured and informal/unprocedural reality, one can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects. This complements the challenge of hypotheses testing at scientific standards of active measures in restoring ecological networks (large-scale ecosystems, landscapes).</p> <p><em><strong>Notes</strong></em></p> <p>[1] For the place of the contributions of this session in the general landscape of the conference, one can download the full report here: <a href="https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/">https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/</a></p> <p>[2] The pdf of his presentation is available for download at <a href="https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/">https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/</a></p> <p><strong>Acknowledgments</strong></p> <p>We thank the organizers of the SERE 2024 conference and the technical team involved in broadcasting and registering this video. We also thank to the persons from the audience who contributed with questions and comments.</p>
IHH - Illuminating the multi-dimensional contributions of Small-scale Fisheries
<p>Publicly available data and codes for the paper "Illuminating the multi-dimensional contributions of small-scale fisheries" by Basurto et al. 2024 (Nature). To produce the main paper figure please go to: https://github.com/DanOvando/ihh_figs</p>
36 Block--63k or 50k scale Maps of South Asia
<p>3 maps of the Survey of India covering block 36.</p>
Active fluctuations of axoneme oscillations scale with number of dynein motors
<p> </p> <p>The dataset published here was used to measure the<strong> phase fluctuations</strong> <strong>(frequency jitter) </strong>of isolated and <strong>reactivated axonemes from <em>Chlamydomonas reinhardtii </em></strong>as a function of the dynein motor density in these axonemes. <br>The corresponding article is published in PNAS: https://doi.org/10.1073/pnas.2406244121</p> <p>Axonemes were isolated from wt(cc-125) and oda1(cc-2228) cells.</p> <p>Reactivation was performed:<br>(1) after dynein extraction with different amounts of salt (KCL) or<br>(2) in the presence of different concentrations of ATP .</p> <p>For all experimental conditions we provide the following data files:<br><br></p> <p><strong>Movies of reactivated axonemes:</strong></p> <p>Reactivated axonemes were imaged with phase-contrast microscopy. Recorded movies are organized in folders (.zip) which are labeled according to the respective reactivation condition (KCL and ATP concentration as well as the <em>Chlamydomonas</em> strain, from which the axonemes were purified, e.g. <strong>50mM_KCl_750uM_ATP_ODA_Extracted.zip</strong>).</p> <p>Those folders contain (1) movie files (multilayer .tif) of single axonemes and (.txt) files with a corresponding number-label (with microscope, camera settings and experimental condition (<em>Chlamydomonas</em> strain, [ATP], [KCL]).</p> <p>Using the number-label, the corresponding data file can be identified. <br><br></p> <p><strong>Data files (for the corresponding movies):</strong></p> <p>The experimental data is organized in MATLAB (.mat) files. Those files contain shape and waveform information (see below) for the respective reactivation condition (axoneme type, [ATP], [KCL] detailed below the file name).</p> <p><strong>WT_KCL_master.mat</strong> - WT axonemes, KCL extracted and reactivated with 750uM ATP</p> <p><strong>0mM_KCl_750uM_ATP_WT_Extracted<br>50mM_KCl_750uM_ATP_WT_Extracted<br>100mM_KCl_750uM_ATP_WT_Extracted<br>200mM_KCl_750uM_ATP_WT_Extracted<br>300mM_KCl_750uM_ATP_WT_Extracted<br>400mM_KCl_750uM_ATP_WT_Extracted<br></strong></p> <p><strong>WT_ATP_master.mat </strong> - WT axonemes reactivated with different ATP concentraions</p> <p><strong>0mM_KCl_50uM_ATP_WT<br>0mM_KCl_100uM_ATP_WT<br>0mM_KCl_370uM_ATP_WT<br>0mM_KCl_500uM_ATP_WT<br>0mM_KCl_750uM_ATP_WT<br></strong></p> <p><strong>ODA_KCL_master.mat </strong>- ODA axonemes, KCL extracted and reactivated with 750uM ATP</p> <p><strong>0mM_KCl_750uM_ATP_ODA_Extracted<br>50mM_KCl_750uM_ATP_ODA_Extracted<br>100mM_KCl_750uM_ATP_ODA_Extracted<br>200mM_KCl_750uM_ATP_ODA_Extracted<br>300mM_KCl_750uM_ATP_ODA_Extracted<br></strong></p> <p><strong>ODA_ATP_master.mat</strong> - ODA axonemes reactivated with different ATP concentraions</p> <p><strong>0mM_KCl_70uM_ATP_ODA<br>0mM_KCl_100uM_ATP_ODA<br>0mM_KCl_370uM_ATP_ODA<br>0mM_KCl_500uM_ATP_ODA<br>0mM_KCl_750uM_ATP_ODA</strong></p> <p>Each file includes a data-cell ‘Master’ with a columns for the different experimental conditions (e.g. concentrations of ATP or KCL). Data-cells contain one structure for each axoneme. </p> <p> </p> <p>These structures have the following fields:</p> <p>nframe… number of frames of the dataset</p> <p>phi_in_rad… phase angle (beat-cycle-phase) (rad)</p> <p>tangent_angle_psi_in_rad… the tangent angle for 24 positions along arc-length (rad)</p> <p>xy_in_micron… the x,y positions (µm)</p> <p>dt_in_second… time between adjacent frames (second)</p> <p>ds_in_micron… spacing between adjacent arc-length positions (µm)</p> <p>f0_in_Hz… beat frequency (Hz)</p> <p>A… beat amplitude (arc-length average) (rad)</p> <p>Q… quality factor</p> <p>sexp… string with experiment label</p> <p>KCL_in_mM… KCL concentration (mM) used for dynein extraction</p> <p>ATP_in_uM… ATP concentration (mM) used for reactivation</p> <p>File… filename, includes a number-label that corresponds to the imaging data provided</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.