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53 results for “value of research”
Research and Clinical Value of New Classification for Premature Ejaculation: Multi-Center Research
ClinicalTrials.gov study NCT02572037. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Data from: Parmotrema internexum (Lecanorales: Parmeliaceae): an overlooked macrolichen in southeastern North America highlights the value of basic biodiversity research
Open the record for dataset details and reuse information.
Figure 6 from: Mattsson BJ, Toth W, Penker M, Kieninger P, Vacik H (2020) Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe. Research Ideas and Outcomes 6: e53608. https://doi.org/10.3897/rio.6.e53608
Figure 6 Gantt chart showing tasks (T), milestones (M), and deliverables (D) as well as involvement of human resources according to the time plan – T, M and D are described in the text.
Figure 4 from: Mattsson BJ, Toth W, Penker M, Kieninger P, Vacik H (2020) Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe. Research Ideas and Outcomes 6: e53608. https://doi.org/10.3897/rio.6.e53608
Figure 4 Example classifications of tradeoffs from the perspective of regional stewardship programs: Distributed: each ecosystem service category is 20-30% (exclusive); Emphasized: ≥ 1 category is 30-50% (exclusive); Dominant: one categories is >50%.
Figure 2 from: Mattsson BJ, Toth W, Penker M, Kieninger P, Vacik H (2020) Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe. Research Ideas and Outcomes 6: e53608. https://doi.org/10.3897/rio.6.e53608
Figure 2 Two hypotheses regarding drivers of adaptation, illustrated by simulated effects of individual drivers on an adaptation index (see below Tasks 1.1, 1.2, 3.2 in the Work Plan). Categories of drivers distinguished by symbols: diamond (u) = science; square (■) = culture; circle (●) = climate; triangle (▲) = cross-border; and × = regional program capacity. Whiskers represent 95% Bayesian credibility intervals; open symbols illustrate significant positive effects. Cx = communication.
Figure 3 from: Mattsson BJ, Toth W, Penker M, Kieninger P, Vacik H (2020) Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe. Research Ideas and Outcomes 6: e53608. https://doi.org/10.3897/rio.6.e53608
Figure 3 Two hypotheses regarding drivers of adaptation, illustrated by simulated values representing absence (A) or presence (B) of interactions between effects on an adaptation index (see below Tasks 1.1, 1.2, 3.2 in Work Plan). Simulated effects include progress toward adaptation by countries of focal regions and by neighbors of these regions. Categories of progress toward adaptation defined as 'more advanced' (at or above median index value) or 'less advanced' (below median index value). Whiskers represent 95% Bayesian credibility intervals; non-overlapping whiskers illustrate statistically significant contrasts.
Figure 1 from: Mattsson BJ, Toth W, Penker M, Kieninger P, Vacik H (2020) Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe. Research Ideas and Outcomes 6: e53608. https://doi.org/10.3897/rio.6.e53608
Figure 1 Candidate drivers of adaptation by a program working at a regional scale, partially adapted from Figures 1.1 and 3.1 in Swart et al. (2009). This conceptual framework provides a basis for constructing hypotheses in this project. Each dashed border encapsulates a category of putative drivers. Neither relationships among individual drivers nor feedbacks between categories of drivers and adaptation actions are shown. Bolded boxes represent drivers that will be examined in this study. Underlined drivers can be at least partly informed from literature sources, whereas the remainder will be based solely on surveys and interviews with regional program administrators. (*Communication can also include coordination of adaptation planning/implementation in other regions).
Figure 5 from: Mattsson BJ, Toth W, Penker M, Kieninger P, Vacik H (2020) Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe. Research Ideas and Outcomes 6: e53608. https://doi.org/10.3897/rio.6.e53608
Figure 5 Hypothetical result of an emphasis on regulating and cultural services consistent with the diverse value tradeoffs hypothesis. General classes of value tradeoffs distinguished by shapes: distributed (u), dominant (●) and double emphasis (■).Whiskers represent 95% Bayesian credibility intervals; open symbol illustrates a significant difference.
Figure 5 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 5 Plot showing the values of b1, b2 (top) and SSE (bottom) for each iteration during the solver optimization.
Figure 2 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 2 The Museum collections of which ~6% are digitised and already have high usage in scientific publications. Data for period February 2015 to October 2021.
Figure 1 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 1 Overview of thematic, investment and efficiency savings approach including five key thematic areas: biodiversity conservation, invasive species, medicines discovery, agricultural research and development, and mineral exploitation.
Supplementary material 1 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Query summary of IUCN Red List Data - data deficient species
Figure 4 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 4 Summary of investment and efficiency approaches to valuing digitisation of Museum collection.
Figure 3 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 3 Valuing pathways to impact across five key areas: biodiversity conservation (£0.7bn–£1bn), invasive species (£0.7bn–£1.1bn), medicines discovery (£0.8bn–£2.8bn), agricultural research and development (£20m–£70m), and mineral exploitation (£20m–£80m). All estimates are in NPV terms over 30 years using a 3.5% discount factor.
Figure 7 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 7 Theory of change showing the four components (inputs, activities, outputs and outcomes) with examples that lead to impact.
Figure 6 from: Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844
Figure 6 Summary of the approach, using a theory of change to guide a literature review and benefits modelling.
Figure 1 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 1 Solver implementation: Objective cell in F4 (minimize sum of squared errors) by changing values of b1 and b2 in cells F2 and F3 respectively.
Supplementary material 1 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
BOD data
Figure 3 from: Hartgerink C, Wicherts J, van Assen M (2016) The value of statistical tools to detect data fabrication. Research Ideas and Outcomes 2: e8860. https://doi.org/10.3897/rio.2.e8860
Figure 3 - Data table from Ruys and Stapel (2008), retracted due to data fabrication. This table includes 15 duplicates (highlighted) in 32 cells, which can be seen as a serious data anomaly that could have been detected with, for example, automated screening procedures.
Figure 1 from: Hartgerink C, Wicherts J, van Assen M (2016) The value of statistical tools to detect data fabrication. Research Ideas and Outcomes 2: e8860. https://doi.org/10.3897/rio.2.e8860
Figure 1 - The applied statistical methods to test for data fabrication in Project 1, depicting those that are combined into an overall test for data fabrication with the Fisher method. Benford's law is excluded from the overall tests because of an expected lack of utility.
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.