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1,453 results for “Outcome research”
Figure 1 from: Hardisty AR, Addink W, Glöckler F, Güntsch A, Islam S, Weiland C (2021) A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo). Research Ideas and Outcomes 7: e67379. https://doi.org/10.3897/rio.7.e67379
Figure 1 Digitally transforming collections science with Digital Specimens and persistent identifiers (PID).
Supplementary material 2 from: Hardisty AR, Addink W, Glöckler F, Güntsch A, Islam S, Weiland C (2021) A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo). Research Ideas and Outcomes 7: e67379. https://doi.org/10.3897/rio.7.e67379
Estimates of numbers of PIDs needed
Supplementary material 4 from: Hardisty AR, Addink W, Glöckler F, Güntsch A, Islam S, Weiland C (2021) A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo). Research Ideas and Outcomes 7: e67379. https://doi.org/10.3897/rio.7.e67379
Dimensions appraisal of the options
Figure 1 from: Ćwiek-Kupczyńska H, Krajewski P (2021) Polish network of research infrastructure for plant phenotyping. Research Ideas and Outcomes 7: e73858. https://doi.org/10.3897/rio.7.e73858
Figure 1 The results of a survey conducted among Polish units involved in plant phenotyping in the part concerning national and international cooperation (n = 32).
Supplementary material 1 from: Eitzel M.V (2021) A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science. Research Ideas and Outcomes 7: e71553. https://doi.org/10.3897/rio.7.e71553
Modeler's Manifesto: Self-Situating Appendix
Figure 3 from: Bernstein A, Moore RS, Rhee LQ, Aronson DL, Katz DL (2021) A digital dietary assessment tool may help identify malnutrition and nutritional deficiencies in hospitalized patients. Research Ideas and Outcomes 7: e70642. https://doi.org/10.3897/rio.7.e70642
Figure 3 Results illustrating a select micronutrient profile which may be within or out of range for a sample patient with the following characteristics: 70 years old, male, height 5 foot 6 inches, weight 125 pounds, minimal physical activity, losing weight, following restrictive diet.
Figure 2 from: Bernstein A, Moore RS, Rhee LQ, Aronson DL, Katz DL (2021) A digital dietary assessment tool may help identify malnutrition and nutritional deficiencies in hospitalized patients. Research Ideas and Outcomes 7: e70642. https://doi.org/10.3897/rio.7.e70642
Figure 2 Results illustrating a select macronutrient profile which may be within or out of range for a sample patient with the following characteristics: 70 years old, male, height 5 foot 6 inches, weight 125 pounds, minimal physical activity, losing weight, following restrictive diet.
Figure 1 from: Eitzel M.V (2021) A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science. Research Ideas and Outcomes 7: e71553. https://doi.org/10.3897/rio.7.e71553
Figure 1 Workflow diagram for manifesto practices, showing which project stages may benefit from which practices. Interdisciplinary fluency, engaging with community-based modeling, and paying attention to power dynamics as well as impacts and implications are all important at all stages of modeling work. Epistemic consistency is important throughout model development (the three middle steps of model choice, construction, and description) and communication, while triangulation and mixed methods contribute largely to model development. The data biography is most important in the model description stage, though one may need to keep a journal and track details of the model development process in order to create the data biography. Treating uncertainty as openness is most important in model communication and application; however, this could feed back into iterative model development steps as well, or one could design models to aid in treating uncertainty as openness.
Figure 2 from: Vyshedskiy A, Dunn R (2015) Mental synthesis involves the synchronization of independent neuronal ensembles. Research Ideas and Outcomes 1: e7642. https://doi.org/10.3897/rio.1.e7642
Figure 2 - On a neurological level, mentally forming the image of Bill Clinton and the lion consists of the following steps: Step 1 - Recall of Bill Clinton: The prefrontal cortex (PFC) activates the ensemble of neurons representing Bill Clinton to fire synchronous actions potentials. Bill Clinton is perceived by the patient. The electrode implanted into the temporal lobe (TL) records an increased rate of action potentials. Step 2 - Recall of the lion: The PFC activates the ensemble of neurons representing the lion to fire synchronous actions potentials. The lion is perceived. The second electrode implanted into the TL records an increased rate of action potentials. Step 3 - The patient mentally integrates the images of Bill Clinton and the lion into one scene. The Mental Synthesis Theory hypothesizes that integration is accomplished by the PFC synchronizing the two neuronal ensembles in time. Step 4 - When synchronization of the Clinton and the lion neuronal ensembles is achieved, a new, never-before-seen mental image of Bill Clinton holding the lion on his lap is perceived by the patient. At that moment the two implanted electrodes are predicted to record synchronous action potentials, implying the synchronization of the Clinton and lion neuronal ensembles.
Figure 1 from: Vyshedskiy A, Dunn R (2015) Mental synthesis involves the synchronization of independent neuronal ensembles. Research Ideas and Outcomes 1: e7642. https://doi.org/10.3897/rio.1.e7642
Figure 1 - Mental synthesis of Bill Clinton holding a lion. Once selective neurons for Bill Clinton and the lion are identified, a subject can be asked to imagine Bill Clinton holding the lion on his lap. The Mental Synthesis theory predicts that both the Clinton neuron and the lion neuron will increase their firing rate and that their activity will be synchronized. Images modified from: 1. William J. Clinton at the Parliament in London, United Kingdom, November 29, 1995. https://commons.wikimedia.org/wiki/File:Bill_Clinton_1995_im_Parlament_in_London.jpg 2. Lioness in the Olomouc Zoo at Svatý kopeček, Czech Republic. This image is licensed under the CC BY-SA license. https://commons.wikimedia.org/wiki/File:Lioness,_Olomouc.jpg
Figure 2 from: Mietchen D, Hagedorn G, Willighagen E, Rico M, Gómez-Pérez A, Aibar E, Rafes K, Germain C, Dunning A, Pintscher L, Kinzler D (2015) Enabling Open Science: Wikidata for Research (Wiki4R). Research Ideas and Outcomes 1: e7573. https://doi.org/10.3897/rio.1.e7573
Figure 2 - Prototype of the platform at the Center for Data Science of Paris-Saclay, where Wikidata identifiers are already used to link public data and public research data.
Figure 1 from: Mietchen D, Hagedorn G, Willighagen E, Rico M, Gómez-Pérez A, Aibar E, Rafes K, Germain C, Dunning A, Pintscher L, Kinzler D (2015) Enabling Open Science: Wikidata for Research (Wiki4R). Research Ideas and Outcomes 1: e7573. https://doi.org/10.3897/rio.1.e7573
Figure 1 - Outline of envisioned platform where Wikidata content can be used within an institutional firewall.
Figure 6 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
Figure 6 - The Nion HERMES can achieve a 9 meV wide (full-width at half-maximum, FWHM) zero loss peak (ZLP), much smaller than the energy distribution of even the best unmonochromated beam produced by the cold field emission electron gun of a cutting-edge UltraSTEM100 microscope. This makes it possible to not only measure very low energy excitations such as phonons (Krivanek et al. 2014), but also significantly reduces the background resulting from the 'tail' of the ZLP at optical and plasmon energies (see Fig. 4/3.1). (Figure courtesy of Tracy Lovejoy / Nion Co.)
Figure 2 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
Figure 2 - Snapshots of a simulated 150 fs trajectory of a C atom that has received 15 eV of kinetic energy from an energetic electron, based on a density functional theory molecular dynamics (Susi et al. 2014). After a complex out-of-plane movement, the silicon-carbon bond is inverted.
Figure 5 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
Figure 5 - Simulations of N and B implantation into graphene (Åhlgren et al. 2011). (a) A schematic illustration of the simulation geometry. (b-d) Probabilities of resulting configurations as functions of the ion energy, characterized from the outcomes of the MD simulations. (Figure courtesy of Jani Kotakoski / University of Vienna.)
Figure 1 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
Figure 1 - a,b) Scanning tunneling microscopy (STM) images of a Cu(111) surface with iron atoms being assembled (a) into a circular quantum corral structure (b) (Crommie et al. 1993). The STM experiments require low temperatures and ultra-high vacuums. c,d) STEM images of a silicon atom embedded in the graphene lattice, being non-destructively moved by one lattice position by a beam-driven silicon-carbon bond inversion (Susi et al. 2014). e,f) The corresponding simulated structures. (Panels a and b courtesy of Michael Crommie / University of California at Berkeley.)
Figure 4 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
Figure 4 - A pictorial illustration of the HeQuCoG project plan, divided into three work packages (WPs, see Section 1.2). The numbered tasks (1.1 to 3.2) are explained in detail in the Methods section below, and citations for images from the literature are given in brackets. Panel captions: 1.1) The implantation of silicon (yellow sphere) into the graphene lattice (black spheres) is simulated via molecular dynamics modeling, yielding optimal ion energies. 1.2) High-quality graphene samples are prepared on Quantifoil TEM grids either from graphene flakes exfoliated onto Si/SiO2 and transferred by immersing the substrate into isopropanol, or from chemically synthesized samples (Meyer et al. 2008). 1.3) Heteroatom ions are accelerated by an electric field, separated by mass, and impacted onto the graphene samples (Schwen 2005). 2.1) Density functional theory and classical potential calculations are used to simulate different configurations of several heteroatoms embedded in the graphene lattice. 2.2) Silicon atoms are moved with atomic precision in the lattice by electron irradiation in a scanning transmission electron microscope (Susi et al. 2014). 3.1) The low-energy electron energy loss spectrum (EELS) of graphene contains collective excitation modes arising from ᴨ and ᴨ+σ plasmons (Zhou et al. 2012). The inset shows the formula for calculating the loss within the GPAW code. 3.2) The influence of heteroatoms embedded in the graphene lattice (left: Z-contrast image) is measured by mapping the EELS response of the ᴨ+σ plasmon (right) (Zhou et al. 2012). With a monochromated electron source, the zero-loss peak is very narrow, allowing lower energy features to be distinguished from the background. (Panel 1.2 courtesy of Jannik Meyer / University of Vienna; panels 3.1 and 3.2 courtesy Juan-Carlos Idrobo / Oak Ridge National Laboratory.)
Figure 3 from: Susi T (2015) Heteroatom quantum corrals and nanoplasmonics in graphene (HeQuCoG). Research Ideas and Outcomes 1: e7479. https://doi.org/10.3897/rio.1.e7479
Figure 3 - Interaction cross sections for relevant electron-beam induced processes at silicon dopant sites based on DFT calculations conducted previously by the principal investigator (Susi et al. 2014).
Figure 6 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 6 - Calman 1906 is available in BLR as https://zenodo.org/record/14941. The taxonomic treatment of Leucon longirostris G.O. Sars (shown above) extracted from this expedition document is also avaible in BLR: https://zenodo.org/record/14942. Both links have unique DOIs assigned to them and thus are also retrievable as https://doi.org/10.5281/zenodo.14941, and https://doi.org/10.5281/zenodo.14942, accordingly.
Figure 2 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 2 - Stations without coordinates (red box) are commonly listed, as well as non-SI units, here: depth as fathoms (based on a slide by Aglaia Legaki, Gabriella Papastefanou and Marilena Tsompanou).
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.