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ShareScore release 0.7.1
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379 results for “data sharing”
Data from: Shared genes but not shared genetic variation: legume colonization by two belowground symbionts
Mutualisms between hosts and multiple symbionts can generate diffuse coevolution if genetic covariance exists between host traits governing multiple interactions. Rhizobia and arbuscular mycorrhizal fungi (AMF) both interact with legume hosts, providing complementary nutrients (nitrogen and phosphorous). Molecular approaches have revealed extensive pleiotropy in the plant genetic pathways required for colonization of both symbionts; however, a quantitative genetic approach is required to understand whether this pleiotropy shapes evolution in natural populations. In a greenhouse experiment with 75 families of Chamaecrista fasciculata grown in two phosphorous environments (fertilized and unfertilized), positive covariance between nodule number and plant aboveground biomass within and across environments indicates selection for increased allocation to rhizobia. Genetic variation for host restriction of AMF colonization in response to P suggests that this aspect of context-dependency can evolve in host populations, and that selection in this mutualism varies with P. Despite the existence of gene-level pleiotropy during rhizobium and AMF infection, we find no evidence for genetic covariance in symbiont colonization or its response to phosphorous - suggesting that genetic variation at other, non-pleiotropic loci govern variation in colonization and thus that these traits likely evolve independently in plant populations.
Data from: It's all in the timing: calibrating temporal penalties for biomedical data sharing
Objective: Biomedical science is driven by datasets that are being accumulated at an unprecedented rate, with ever-growing volume and richness. There are various initiatives to make these datasets more widely available to recipients who sign Data Use Certificate agreements, whereby penalties are levied for violations. A particularly popular penalty is the temporary revocation, often for several months, of the recipient's data usage rights. This policy is based on the assumption that the value of biomedical research data depreciates significantly over time; however, no studies have been performed to substantiate this belief. This study investigates whether this assumption holds true and the data science policy implications. Methods: This study tests the hypothesis that the value of data for scientific investigators, in terms of the impact of the publications based on the data, decreases over time. The hypothesis is tested formally through a mixed linear effects model using approximately 1200 publications between 2007 and 2013 that used datasets from the Database of Genotypes and Phenotypes, a data-sharing initiative of the National Institutes of Health. Results: The analysis shows that the impact factors for publications based on Database of Genotypes and Phenotypes datasets depreciate in a statistically significant manner. However, we further discover that the depreciation rate is slow, only ∼10% per year, on average. Conclusion: The enduring value of data for subsequent studies implies that revoking usage for short periods of time may not sufficiently deter those who would violate Data Use Certificate agreements and that alternative penalty mechanisms may need to be invoked.
Data from: Testing and interpreting the shared space-environment fraction in variation partitioning analyses of ecological data
Variation partitioning analyses combined with spatial predictors (Moran's eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space-environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially-constrained null models: torus-translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly-distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (< 0.05). The statistical power was high (> 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine-scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.
Data Sharing for: ''RNA folding landscapes from explicit solvent all-atom simulations"
<p>Data required to reproduce the figures and analysis in the paper: ''RNA folding landscapes from explicit solvent all-atom simulations".</p>
Data from: A multi-breed genome-wide association analysis for canine hypothyroidism identifies a shared major risk locus on CFA12
[No abstract entered]
Figure 1 from: Neylon C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773. https://doi.org/10.3897/rio.3.e21773
Figure 1 - The Institutional Analysis and Design framework adapted from (Ostrom 2005).
Figure 1 from: Irawan D, Rachmi C (2018) Promoting data sharing among Indonesian scientists: A proposal of generic university-level Research Data Management Plan (RDMP). Research Ideas and Outcomes 4: e28163. https://doi.org/10.3897/rio.4.e28163
Figure 1 Current situation of data lifecycle (Irawan 2018).
Figure 7 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 7 - Mobile app for sporadic observations reporting.
Figure 2 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 2 - The Plazi workflow (green) within EU BON.
Data sharing of Trace element partitioning between apatite and silicate melts: Effects of major element composition, temperature, and oxygen fugacity, and implications for the volatile element budget of the lunar magma ocean
<p>This repository contains all data used in <strong>Ji and Dygert (2024)</strong>, along with two essential tools (Apatite_Kd_calculator.xlsx):</p> <p>1. A calculator for apatite trace element partition coefficients</p> <p>2. A Eu-in-apatite–plagioclase oxybarometer</p> <p><strong>Update (2025-07-15 version):</strong></p> <p>This version corrects a minor typo in the molecular weight of SiO₂ used in both tools. While the original error had a negligible effect on the calculated partition coefficients, this correction ensures full accuracy for future applications.</p>
Duke COVID-19 Shared Data and Specimen Repository
ClinicalTrials.gov study NCT04368234. IPD Sharing: NO. Countries: 1. Publications: 0.
REal-time Data Monitoring for Shared Adaptive, Multi-domain and Personalised Prediction and Decision Making for Long-term Pulmonary Care Ecosystems (RE-SAMPLE)
ClinicalTrials.gov study NCT04955080. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Anonymous Data Sharing for Small Bowel
ClinicalTrials.gov study NCT06868875. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Ethical Challenges of Consent in Data Sharing
ClinicalTrials.gov study NCT03346720. IPD Sharing: YES. Countries: 1. Publications: 0.
Sharing Digital Self-Monitoring Data With Others to Enhance Long-Term Weight Loss
ClinicalTrials.gov study NCT05180448. IPD Sharing: NO. Countries: 1. Publications: 0.
Large Scale ICU Data Sharing for 1000 Critically Ill Patients With Severe Acute Respiratory Distress Syndrome Coronavirus (SARS.COV19) in Three Distinct Isolation Centers
ClinicalTrials.gov study NCT04779268. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deciphering shared molecular dysregulation across Parkinson's Disease variants using a multi-modal network-based data integration and analysis
GEO Series GSE276684. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Data from: Testing and interpreting the shared space-environment fraction in variation partitioning analyses of ecological data
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Data from: Data management, archiving and sharing for biologists and the role of research institutions in the technology-oriented age
Open the record for dataset details and reuse information.
Data from: Shared evolutionary origin of MHC polymorphism in sympatric lemurs
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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.