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.
12,237
datasets available to search
ShareScore release 0.9.0
Dataset results
12,237 results for “2019”
The Maintenance of Long-Lived Concentric Eyewall in Simulated Typhoon Lekima (2019)
<p>The model-simulated data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (qnn_nancy@yahoo.com).</p>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
Dataset from: Honert and Zelik (2019) "Foot and shoe responsible for majority of soft tissue work in early stance of walking"
<p>Raw and processed full body kinematics and kinetics for Honert and Zelik, 2019, "Foot and shoe responsible for majority of soft tissue work in early stance of walking" <em>Human Movement Science.</em></p>
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
TESS-JANUARY-2019
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). January 2019</p>
Löhr-Limpens, M., Göhringer, F., Schenk, T., & Hesse, C. (2019). 'Grasping and perception are both affected by irrelevant information and secondary tasks: new evidence from the Garner paradigm'. Psychological Research. DOI: 10.1007/s00426-019-01151-z
<p>This repository contains 6 data files and one readme file describing the content of the data files.</p> <p> </p>
Images used for protein quantification in Hayes et al (2019) Current Biology: Figure 3
<p>Images used for quantification of PIF4-HA and PIF5-HA protein stability in Fig 3. Hayes et. al 2018.</p> <p><em>35S:PIF4-HA </em>and <em>35S:PIF5-HA</em> (L<em>er</em>) were germinated on ½ MS plates in white light (16:8h photoperiod) for 3 days, before being transferred to plates with or without 75mM NaCl. Plates were then grown in red light or a further 2 days. On day 5, at Zt 3, half the plates were moved to red+ far-red light. Tissues were harvested at Zt 4. 10 seedlings were homogenized and proteins were extracted in 70 µl Cracking Buffer (125mM Tris pH 7.4, 2% SDS, 10% Glycerol, 6M Urea, 5% βME) and 15 µl of the extract was run on a 10% polyacrylamide gel. Blots were probed with anti-GFP-HRP (1:1000). Membranes were developed with 50/50 ‘pico’ and ‘femto’ chemiluminescence substrate (Thermo) on a ChemiDoc (Biorad).</p> <p> </p> <p>Experiment was performed on 4 dates (in duplicate).</p> <p>R= red light</p> <p>FR= red +far-red light</p> <p>RN= red light +NaCl</p> <p>FRN= red +far-red light +NaCl</p> <p> </p> <p>Sample order is as follows:</p> <p> </p> <p>01-08-18:</p> <p>PIF4-HA: R1, R2, FR1, FR2, RN1, RN2, FRN1, FRN2</p> <p>PIF5-HA: R1, R2, FR1, FR2, RN1, RN2, FRN1, FRN2</p> <p> </p> <p>08-08-18:</p> <p>PIF4-HA: R3, R4, FR3, FR4, RN3, RN4, FRN3, FRN4</p> <p>PIF5-HA: R3, R4, FR3, FR4, RN3, RN4, FRN3, FRN4</p> <p> </p> <p>15-08-18:</p> <p>PIF4-HA: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6</p> <p>PIF5-HA: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6</p> <p> </p> <p>10-10-18:</p> <p>PIF4-HA: R7, FR7, RN7, FRN7, R8, FR8, RN8, FRN8</p> <p>PIF5-HA: R7, FR7, RN7, FRN7, R8, FR8, RN8, FRN8</p>
Image used for GUS staining quantification in Hayes et al (2019) Current Biology: Figure 3
<p><em>DR5v2:GUS</em> plants in the Col or <em>pif4-101</em>/<em>pif5</em> background were germinated for 3 days on soil (white light- 16:8h photoperiod), before being transferred to soil with or without 25mM NaCl. Plants were then grown in Wl for a further day. On day 4, at Zt 4.5, half the plants were moved to white light + far-red light. Tissues were fixed at Zt 4.5 on day 6 by 20 minutes in 90% acetone at -20°C. Tissues were washed twice under a vacuum in GUS wash solution (0.1M Phospho-PI pH 7.0, 10 mM EDTA, 2 mM K3Fe(CN)6) and then stained overnight at 37°C in GUS staining solution (0.1M Phospho-PI pH 7.0, 10 mM EDTA, 1 mM K3Fe(CN)6, 1mM K4Fe(CN)6 * 3H2O, 0.5 mg/ ml X-Gluc). The GUS reaction was inhibited by treatment with 3:1 ethanol: acetic acid mix for 1 hour at 37°C. Tissues were cleared with 70% ethanol over several days and then mounted in 10% chloral hydrate, 30% glycerol. Slides were scanned and the images used for quantification.</p> <p> </p> <p>Sample order is as follows:</p> <p> </p> <p>pif4pif5 Wl Col Wl</p> <p>pif4pif5 Wl Col Wl</p> <p>pif4pif5 Wl+ NaCl Col Wl+ NaCl</p> <p>pif4pif5 Wl+ NaCl Col Wl+ NaCl</p> <p>pif4pif5 Wl +FR Col Wl +FR </p> <p>pif4pif5 Wl +FR Col Wl +FR </p> <p>pif4pif5 Wl +NaCl +FR Col Wl +NaCl +FR</p> <p>pif4pif5 Wl +NaCl +FR Col Wl +NaCl +FR</p>
Images used for protein quantification in Hayes et al (2019) Current Biology: Figure 4
<p>Images used for quantification of BES1-GFP protein stability in Fig 4. Hayes et. al 2019.</p> <p><em>35S:BES1-GFP</em> plants were germinated on ½ MS plates in white light (16:8h photoperiod) for 3 days, before being transferred to plates with or without 75mM NaCl. Plates were then grown in red light or a further 2 days. On day 5, at Zt 3, half the plates were moved to red+ far-red light. Tissues were harvested at Zt 4. 10 seedlings were homogenized and proteins were extracted in 70 µl Cracking Buffer (125mM Tris pH 7.4, 2% SDS, 10% Glycerol, 6M Urea, 5% βME) and 15 µl of the extract was run on a 10% polyacrylamide gel. Blots were probed with anti-GFP-HRP (1:1000). Membranes were developed with 50/50 ‘pico’ and ‘femto’ chemiluminescence substrate (Thermo) on a ChemiDoc (Biorad).</p> <p> </p> <p>Experiment was performed on 3 dates (in duplicate).</p> <p>R= red light</p> <p>FR= red +far-red light</p> <p>RN= red light +NaCl</p> <p>FRN= red +far-red light +NaCl</p> <p> </p> <p>Sample order is as follows:</p> <p> </p> <p>01-08-18:</p> <p>BES1-GFP: R1, FR1, RN1, FRN1, R2, FR2, RN2, FRN2</p> <p> </p> <p>08-08-18:</p> <p>BES1-GFP: R3, FR3, RN3, FRN3, R4, FR4, RN4, FRN4</p> <p> </p> <p>15-08-18:</p> <p>BES1-GFP: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6</p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</p>
Datenanhang zu Ergebnisse der ZKI Top Concerns-Umfrage des ZKI-Arbeitskreises Strategie und Organisation für das Jahr 2019
<p>Der Arbeitskreis Strategie und Organisation des ZKI-Vereins führt eine jährliche Umfrage zu den wichtigsten Themen und Schwerpunkten der Mitgliedseinrichtungen durch. Im Jahr 2016 wurde die Umfrage auf eine elektronische Umfrage im Web mittels Lime-Survey umgestellt. Für das Jahr 2019 wurde die Umfrage mit einem zusätzlichen Fragenkatalog zum Schwerpunkt „Personal“ erweitert.</p> <p>Diese Datei enthält die aufbereiteten Rohdaten der Ergebnisse der ZKI Top Concerns-Umfrage für das Jahr 2019.</p>
Dataset accompanying Riesch et al. 2019. Grazing by wild red deer: management options for the conservation of semi-natural open habitats. Journal of Applied Ecology
<p>This repository contains vegetation biomass and forage quality data used by Riesch et al. in an article accepted in Journal of Applied Ecology.</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original article.</p>
Camera trap image of Lepus europaeus (2019-02-01T14:20:22Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Sitta europaea (2019-02-03T12:53:45Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Corvus corone (2019-02-04T16:23:55Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Apodemus sylvaticus (2019-01-19T01:10:31Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Replication Kit for the Paper "Alternative Interaction Methods for VR Environments with Leap Motion" submitted to UIST 2019
<p>This file contains the replication kit for the paper "Alternative Interaction Methods for VR Environments with Leap Motion" submitted to the "32nd ACM User Interface Software and Technology Symposium (UIST 2019)". It consists of the Virtual Reality implemented for the case study of the paper as well as all introductory material for participants, measurements, and analysis scripts.</p>
Camera trap image of Parus major (2019-01-12T10:28:29Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Sciurus vulgaris (2019-02-11T10:55:51Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Fringilla coelebs (2019-01-28T13:50:09Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
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.