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1,453 results for “Outcome research”
Project factors and conservation outcome data for SeaDoc Society-funded research projects
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Core outcome set for burn care research: Delphi survey data
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Figure 2 from: Moldovan OT, Øvrevik Skoglund R, Banciu HL, Dinu Cucoș A, Levei EA, Perșoiu A, Lauritzen S-E (2019) Monitoring and risk assessment for groundwater sources in rural communities of Romania (GROUNDWATERISK). Research Ideas and Outcomes 5: e48898. https://doi.org/10.3897/rio.5.e48898
Figure 2 Gantt Chart of the proposed activities during the project (June 2019 – May 2023). PR = Phase Report, FR = Final Report; *only 1 month (1) or 2 months (17).
Figure 3 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 3 Types of campus groups that provide RDS (n=103). Type codes are defined as follows: Admin: a campus administrative unit that does not fall into any other category; Center: research centers or institutes excluding HPC groups; Dept = Departments or colleges; HPC: High Performance Computing and research computing units including HPC run by IT units; Individuals: Individual staff, faculty, students, etc.; IT: Information Technology associated with the entire campus, colleges, or departments excluding HPC groups; Lab: Various labs on campus that do not fall into any other category; Research Office: Groups that oversee university research; Other: Groups that cannot be categorized under any other code.
Figure 2 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 2 Breakdown of the workshops or topics with a tool or programming language code applied (n=47). Only tool codes that have a frequency >1 are shown. Tool code names are self-explanatory (i.e. the name of tool).
Figure 1 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 1 Workshop topic code frequencies. Up to two topic codes were applied to each workshop (n=160). Topic codes are defined as follows: Carpentry: a data or software Carpentry workshop; Cleaning: data cleaning and related techniques; Coding: how to work with data via command line or in a specific language; General: the basics of data management; GIS: geographic information system or spatial data/tools; Grants: the word "grants" or the name of a funding agency was explicitly mentioned in the workshop's title or description; HPC: high performance computing; Locate: focused on how to search and locate datasets; Metadata: metadata and data documentation; Mining: focused on text and data mining; Org: data organization; Other: misc. topics or unclassifiable; Plans: data management plans; Repository: addresses a specific repository, how to use a repository, or data repositories in general; Reproducibility: focused on research reproducibility; StorageSec: data storage and/or security tools and topics; Tool: focused on how to use tools related to data and data management (see Fig. 2); Visualization: data visualization.
Supplementary material 1 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Links to library and university/college research data management policies.
Figure 4 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 4 Disciplinary categorization of campus groups that provide RDS (n=34). Discipline codes are defined as follows: Bio: Groups that specialize in biology, including health and medicine; Bio/Stats: Groups that specialize in biology and statistics; Data: no specific discipline but has the word 'data' in the name; GIS: Groups that specialize in spatial and GIS (Geographic Information Systems) data; Humanities: Groups specializing in humanities; Social/Stats: Groups that specialize in statistics and social science; SocialSci: Groups specializing in social science; Stats: Groups specializing in statistics.
Figure 3 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 3 Three types of alpha waves. The arrows indicate the beginning and the end of high activity in a column. The horizontal lines represent the duration of activity at the high level. The small blue bars indicate that the initial part of the downward slope often has a decisive start. The yellow and purple bars indicate that the upward slope is less steep than the downward slope. The vertical lines illustrate how the wavelength was measured. The EEGs are from participants 59, 51, and 149 in a previous study (Johannisson 2016).
Figure 9 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 9 Overview for number and duration. The values in the center of the figure are thought to be optimal and associated with good health. Slightly suboptimal values may lead to anxiety and depression. Values clearly outside the optimal ranges are expected to cause mental disorders. Optimal values for children are not the same as those for adults. During epileptic seizures, number and duration are changed in a special way.
Figure 6 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 6 Variation in wavelength at 10.0 Hz. The blue and green symbols indicate elements that are repeated at an interval of five waves. The pattern was not clear when there was almost no variation in wavelength, as in the middle of the sequence. Fortunately for the analysis, this period with almost no variation was not long-lasting. The letter H refers to disturbances from the heart. The EEG is from a 29-year-old male.
Figure 2 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 2 Number of highly active columns and EEG frequency. The histogram shows the distribution of the frequency for 213 individuals. The data are from a pilot study described in another paper (Johannisson 2016). In the lower part of the figure, the data have been slightly smoothed and presented as a curved line. This makes it easier to see that there are different frequency groups. The smallest possible number of highly active columns should be found when the limits are set to 0 and 1. This may correspond to the first peak on the left. The other peaks can then be linked to higher limits in consecutive order.
Figure 5 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 5 Variation in wavelength at 9.5 Hz. The red and blue symbols indicate a biphasic pattern where the elements are repeated at an interval of five waves. The EEG is from a 26-year-old female.
Figure 10 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 10 Variation in wavelength at 5.6 Hz. The green and yellow symbols indicate a pattern where the elements are repeated at an interval of four waves. The data are from a 25-year-old male.
Figure 1 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 1 The two levels hypothesis. The colorful horizontal lines represent the duration of high activity in individual columns. The vertical lines show the time relation to the alpha waves. The distance between two neighboring lines is the wavelength, and the green symbols indicate that some waves are a little bit shorter than other waves.
Supplementary material 1 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Data S1. EEGs for Figs. 4–7 and 10.
Figure 4 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 4 Variation in wavelength at 8.3 Hz. The green and blue symbols indicate that there is a monophasic pattern and that the interval for the repeated elements is four waves. The x-axis in the upper diagram has the same timescale as the EEG. For statistical analysis, the x-axis in the lower diagram is a category axis. The colorful horizontal lines illustrate the hypothesis that was tested. The data are from a 22-year-old female.
Figure 7 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 7 Variation in wavelength at 11.2 Hz. The red, green, and blue symbols indicate a relatively complicated pattern wherein the elements are repeated at an interval of six waves. The EEG is from a 28-year-old female.
Figure 8 from: Johannisson T (2020) Variation in length of alpha waves reveals how forebrain activity is organized. Research Ideas and Outcomes 6: e49942. https://doi.org/10.3897/rio.6.e49942
Figure 8 The starting point for the two levels hypothesis. A hand has an inside and an outside, and both sides are available for observation. In a similar way, the activity in some parts of the brain can be observed from two different angles.
Figure 3 from: Müller C, Bräutigam A, Eilers EJ, Junker RR, Schnitzler J-P, Steppuhn A, Unsicker SB, van Dam NM, Weisser WW, Wittmann MJ (2020) Ecology and Evolution of Intraspecific Chemodiversity of Plants. Research Ideas and Outcomes 6: e49810. https://doi.org/10.3897/rio.6.e49810
Figure 3 Scheme of the collaborative ring trial within the RU. For details see text. C – control; H – herbivore-treated.
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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.