Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “user manual”
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
EXCEED-DMv1.0.0: User Manual Results for Si and Ge
<p>DM-electron interaction rates in Si and Ge targets used for the user manual: <a href="https://arxiv.org/abs/2210.14917">[2210.14917] EXCEED-DM: Extended Calculation of Electronic Excitations for Direct Detection of Dark Matter (arxiv.org)</a>. All plots in the user manual can be reproduced from the data here. File names which only differ by "_<number>" are the same calculation, split for different DM mass points.</p> <p> </p> <p>Calculations included/folder structure is:</p> <p>- (Si/Ge)/ - Calculations for Si/Ge targets.</p> <p> - scatter/ - Binned scattering rate calculations.</p> <p> - dark_photon/ - Kinetically mixed dark photon.</p> <p> - screening/ - Different approximations about f_scr, the screening factor</p> <p> - no_scr/ - No screening</p> <p> - ana_scr/ - Analytic screening</p> <p> - num_scr/ - Numeric screening</p> <p> - annual_modulation - Calculations for the annual modulation of the DM-electron scattering rate, screened with numerically computed dielectric.</p> <p> - abs/ - Absorption rate calculations</p> <p> - vector/ - vector DM</p> <p> - scalar/ - scalar DM</p> <p> - ps/ - pseudoscalar DM</p> <p> - dielectric/ - Numerically computed dielectric</p>
Manually Annotated Event Log of Users Prompts in LLMs for Conceptual Modeling
<p>This repository contains the supplementary material for our paper at ER 2024 Conference. </p> <p>The data contains the results of an empirical study with 76 undergraduate information systems students. The students submitted the course assignments in 39 groups (of one or two students). The assignment used for the study required use case modeling with UML use case diagrams and domain modeling with UML class diagrams. The groups were first expected to interact with an LLM and then, if needed, to manually improve their models. Groups were randomly assigned to interact with either GPT 4.0 or Code Llama 34B Instruct in one of three application domains. </p> <p>The participants were instructed to engage with the LLM until they were satisfied with the results or opted to skip further refinement. The interaction log contains the following fields: User ID, Input (the user prompt), Response (the modeling artifacts), and the Prompt Number (within user ID).</p>
Overground Walking Program With Robotic Exoskeleton in Long-term Manual Wheelchair Users With Spinal Cord Injury
ClinicalTrials.gov study NCT03989752. IPD Sharing: YES. Countries: 1. Publications: 2.
Effects of PNF on Shoulder Pain in Paraplegic Manual Wheel Chair Users
ClinicalTrials.gov study NCT04967911. IPD Sharing: NO. Countries: 1. Publications: 7.
Shoulder Kinematics and Acute Ultrasonographic Changes in Manual Wheelchair Users With Spinal Cord Injury
ClinicalTrials.gov study NCT04712188. IPD Sharing: NO. Countries: 1. Publications: 1.
Analysis of the Motion Activity Manual Wheelchair Users
ClinicalTrials.gov study NCT04285970. IPD Sharing: NO. Countries: 1. Publications: 2.
Effectiveness of Wheelchair Skills Training for Caregivers of Manual Wheelchair Users
ClinicalTrials.gov study NCT03856749. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Exercise Training and Manual Wheelchair Users With SCI
ClinicalTrials.gov study NCT03727893. IPD Sharing: Not stated. Countries: 1. Publications: 28.
Evaluation of a Smartphone-delivered Peer-led Physical Activity Counselling Program for Manual Wheelchair Users
ClinicalTrials.gov study NCT02826707. IPD Sharing: NO. Countries: 1. Publications: 1.
Motor Learning Approach for Manual Wheelchair Users
ClinicalTrials.gov study NCT03781609. IPD Sharing: Not stated. Countries: 1. Publications: 106.
Effects of Axle Positions on Wheelchair Skills in Manual Wheelchair Users
ClinicalTrials.gov study NCT00949143. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Physical Performance Measures for Manual Wheelchair Users
ClinicalTrials.gov study NCT00011544. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Natural History of Shoulder Pathology in Manual Wheelchair Users
ClinicalTrials.gov study NCT02600910. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Improving Clinician Capacity to Provide Interventions for Manual Wheelchair Users
ClinicalTrials.gov study NCT06294834. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Biomechanics of Adaptive Rowing in Active and Inactive Manual Wheelchair Users
ClinicalTrials.gov study NCT05114629. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Safe and Effective Shoulder Exercise Training in Manual Wheelchair Users With SCI
ClinicalTrials.gov study NCT02777281. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Participation of Manual Wheelchair Users in the Community
ClinicalTrials.gov study NCT01301846. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.