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.
261
datasets available to search
ShareScore release 0.9.0
Dataset results
261 results for “Research assessment”
Figure 2 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 2 - Composition of South African (A, C, E) and Cape Floristic Region (B, D, F) katydid assemblages as characterised by their distribution (A, B), mobility (C, D), and trophic level (E, F) relative to their IUCN threat status.
Figure 1 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 1 - Proportion of South African (A, C, E, G) and Cape Floristic Region (B, D, F, H) katydid assemblages as characterised by the KBI assessment criteria (Threat Status, Distribution, Trophic level and Mobility).
Figure 5 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 5 - Map of ecosystem threat statuses and the average KBI scores (i.e. KBI/Site) of each ecosystem.
Figure 4 from: Schmitt M, Uhl G (2017) Oulema septentrionis and O. erichsonii are neither conspecific nor melanic variants of O. melanopus as assessed by micro CT analysis of their lectotypes (Insecta, Coleoptera, Chrysomelidae, Criocerinae). In: Chaboo CS, Schmitt M (Eds) Research on Chrysomelidae 7. ZooKeys 720: 121-130. https://doi.org/10.3897/zookeys.720.19760
Figure 4 - Oulema erichsonii , lectotype; a median lobe with flagellum lateral b apex of median lobe with flagellum dorsal c flagellum (not to scale). 3D reconstructed microCT-scans.
Figure 2 from: Schmitt M, Uhl G (2017) Oulema septentrionis and O. erichsonii are neither conspecific nor melanic variants of O. melanopus as assessed by micro CT analysis of their lectotypes (Insecta, Coleoptera, Chrysomelidae, Criocerinae). In: Chaboo CS, Schmitt M (Eds) Research on Chrysomelidae 7. ZooKeys 720: 121-130. https://doi.org/10.3897/zookeys.720.19760
Figure 2 - Oulema septentrionis , lectotype; a: median lobe with flagellum lateral b apex of median lobe with flagellum dorsal; c: flagellum (not to scale). 3D reconstructed microCT-scans.
Figure 3 from: Schmitt M, Uhl G (2017) Oulema septentrionis and O. erichsonii are neither conspecific nor melanic variants of O. melanopus as assessed by micro CT analysis of their lectotypes (Insecta, Coleoptera, Chrysomelidae, Criocerinae). In: Chaboo CS, Schmitt M (Eds) Research on Chrysomelidae 7. ZooKeys 720: 121-130. https://doi.org/10.3897/zookeys.720.19760
Figure 3 - Oulema erichsonii (Suffrian, 1841), lectotype. a dorsal b lateral from left c labels. Scale bars: 1 mm.
Figure 1 from: Schmitt M, Uhl G (2017) Oulema septentrionis and O. erichsonii are neither conspecific nor melanic variants of O. melanopus as assessed by micro CT analysis of their lectotypes (Insecta, Coleoptera, Chrysomelidae, Criocerinae). In: Chaboo CS, Schmitt M (Eds) Research on Chrysomelidae 7. ZooKeys 720: 121-130. https://doi.org/10.3897/zookeys.720.19760
Figure 1 - Oulema septentrionis (Weise, 1880), lectotype. a dorsal b lateral from left c labels. Scale bars: 1 mm.
Figure 1 from: Underwood E, Taylor K, Tucker G (2018) The use of biodiversity data in spatial planning and impact assessment in Europe. Research Ideas and Outcomes 4: e28045. https://doi.org/10.3897/rio.4.e28045
Figure 1 Data use within the EIA process Own compilation based on information in (King et al. 2012).
Figure 2 from: Salvi D, D'Alessandro P, Biondi M (2019) Host plant associations in Western Palaearctic Longitarsus flea beetles (Chrysomelidae, Galerucinae, Alticini): a preliminary phylogenetic assessment. In: Schmitt M, Chaboo CS, Biondi M (Eds) Research on Chrysomelidae 8. ZooKeys 856: 101-114. https://doi.org/10.3897/zookeys.856.32430
Figure 2 Maximum Likelihood phylogenetic tree of 52 species of Longitarsus based on concatenated cox1 and 16S DNA sequences. Circles in correspondence of nodes represent bootstrap support (BS, upper half) and posterior probability (BPP, bottom half) from Bayesian analysis: black for BS > 90 and BPP > 0.98; grey for BS of 70–90% and BPP of 0.95–0.98; white for BS of 50–70% only for nodes supported by Bayesian analysis. Abbreviations: POL = polyphagous; ? = host plants unknown.
Figure 1 from: Salvi D, D'Alessandro P, Biondi M (2019) Host plant associations in Western Palaearctic Longitarsus flea beetles (Chrysomelidae, Galerucinae, Alticini): a preliminary phylogenetic assessment. In: Schmitt M, Chaboo CS, Biondi M (Eds) Research on Chrysomelidae 8. ZooKeys 856: 101-114. https://doi.org/10.3897/zookeys.856.32430
Figure 1 Percent distribution of oligophagous and monophagous Western Palaearctic species of Longitarsus on host plant families.
Scientometric Assessment of Digital Library Research Publications during 2008-2017 using SCOPUS Database
Open the record for dataset details and reuse information.
Autism spectrum disorder in architecture perspective: a review of the literature and bibliometric assessment of research indexed in Web of Science
<p>This data downloaded from Web of Science.</p>
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 1b from: Kotze D, Brandmayr P, Casale A, Dauffy-Richard E, Dekoninck W, Koivula M, Lovei G, Mossakowski D, Noordijk J, Paarmann W, Pizzoloto R, Saska P, Schwerk A, Serrano J, Szyszko J, Taboada Palomares A, Turin H, Venn S, Vermeulen R, Zetto Brandmayr T (2011) Forty years of carabid beetle research in Europe – from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation. ZooKeys 100: 55-148. https://doi.org/10.3897/zookeys.100.1523
Figure 1b - Front covers of the first European meetings, ECM 1–8 and that of Hamburg 1984 (centre cover) (see also Table 2).
Figure 2 from: Kotze D, Brandmayr P, Casale A, Dauffy-Richard E, Dekoninck W, Koivula M, Lovei G, Mossakowski D, Noordijk J, Paarmann W, Pizzoloto R, Saska P, Schwerk A, Serrano J, Szyszko J, Taboada Palomares A, Turin H, Venn S, Vermeulen R, Zetto Brandmayr T (2011) Forty years of carabid beetle research in Europe – from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation. ZooKeys 100: 55-148. https://doi.org/10.3897/zookeys.100.1523
Figure 2 - Different pitfall types. A = Jar or yoghurt can. B and C = traps with an outer can to make collecting of the sample easier. B = funnel trap with small jar. C = trap for moist biotopes (the outer can contains gravel or stones to prevent the can from being pushed up by groundwater). V = preservative (usually formaldehyde 3–4% or propylene glycol), S = stones or gravel.
Figure 1a from: Kotze D, Brandmayr P, Casale A, Dauffy-Richard E, Dekoninck W, Koivula M, Lovei G, Mossakowski D, Noordijk J, Paarmann W, Pizzoloto R, Saska P, Schwerk A, Serrano J, Szyszko J, Taboada Palomares A, Turin H, Venn S, Vermeulen R, Zetto Brandmayr T (2011) Forty years of carabid beetle research in Europe – from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation. ZooKeys 100: 55-148. https://doi.org/10.3897/zookeys.100.1523
Figure 1a - Participants of the first European Carabidologist Meeting in Wijster, 1969. From left to right: Vlijm, Van der Aart, Lindroth, Stein, Wijmans, Hengeveld, Palmén, Van Dijk, Richter, Venema, Mook, Thiele, Tjallingii, Den Boer, Haeck, Neumann, Meijer.
Figure 1c from: Kotze D, Brandmayr P, Casale A, Dauffy-Richard E, Dekoninck W, Koivula M, Lovei G, Mossakowski D, Noordijk J, Paarmann W, Pizzoloto R, Saska P, Schwerk A, Serrano J, Szyszko J, Taboada Palomares A, Turin H, Venn S, Vermeulen R, Zetto Brandmayr T (2011) Forty years of carabid beetle research in Europe – from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation. ZooKeys 100: 55-148. https://doi.org/10.3897/zookeys.100.1523
Figure 1c - Front covers of the last five ECMs and of a few major carabidology publications (Thiele 1977; Ball et al. 1998; Erwin et al. 1979; Noonan et al. 1992) (see also Table 2).
Figure 9 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
Figure 2 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 2 - Mockup of audio recording annotation tool – Step 1: Selection. This figure shows a mockup of what an audio annotation Web application tool could look like. In this first step, (A) the Worker presses the Play icon to listen to the voice recording, (B) selects a problematic segment by clicking and dragging the mouse over the waveform, and (C) replays the recording if necessary and selects other problematic segments.
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.