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.
1,899
datasets available to search
ShareScore release 0.9.0
Dataset results
1,899 results for “Running”
Cooling run (years 0267-0514) from Eisenman & Armour (2024)
<p>The following information is also included in the README.txt file accompanying the data.</p> <p><br>Citation for data:</p> <p>I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p><br>Description of data:</p> <p>The Warming and Cooling simulations are both branched from the end of year 500 of a preexisting CESM2 pre-industrial (PI) simulation ("b.e21.B1850.f09_g17.CMIP6-piControl.001" by Danabasoglu et al., 2020). The kernel analysis uses the mean annual cycle averaged over years 480-499 of this PI simulation as the reference climate. Output from this PI simulation can be downloaded from<br>https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.b.e21.B1850.f09_g17.CMIP6-piControl.001.atm.proc.monthly_ave.html</p> <p>For the first 150 years of the Warming run, we use a preexisting CESM2 simulation with CO2 increasing at 1%/year ("b.e21.B1PCT.f09_g17.CMIP6-1pctCO2.001" by Danabasoglu, 2019); data from this simulation is shared elsewhere and is mirrored here for convenience. This is followed by our simulation "WARMING1ppyCESM2cheyenne", which we ran until it failed with an error after year 0279. Hence the data for the Warming run comes from the following simulations:<br>- Year 0001-0150: b.e21.B1PCT.f09_g17.CMIP6-1pctCO2.001<br>- Year 0151-0279: WARMING1ppyCESM2cheyenne</p> <p>For the Cooling run, we decreased CO2 at 1%/year in the simulation "COOLING1ppyCESM2cheyenne". After year 140, we adjusted the processor (PE) layout for a more economical (but slower) load balancing, which required us to branch to a new case, "COOLING1ppyCESM2cheyenne2". This simulation failed with an error after year 278, and we commented out a line in the model code and re-ran starting from 12 years earlier using the case name "COOLING1ppyCESM2cheyenne3". This simulation failed with an error after year 332, and we commented out another line in the model code and re-ran starting in year 333 using the case name "COOLING1ppyCESM2cheyenne5" (there is no simulation "COOLING1ppyCESM2cheyenne4" in this collection). This simulation failed with an error after year 514. Hence the data for the Cooling run comes from the following simulations:<br>- Year 0001-0140: COOLING1ppyCESM2cheyenne<br>- Year 0141-0266: COOLING1ppyCESM2cheyenne2 [ignore the additional years 0267-0278 in this simulation)<br>- Year 0267-0332: COOLING1ppyCESM2cheyenne3<br>- Year 0333-0514: COOLING1ppyCESM2cheyenne5</p> <p>Further details regarding these simulations are given in the "Methods" section of the paper (Eisenman & Armour, 2024).</p> <p><br>Downloading the data:</p> <p>Model output from the Warming and Cooling runs, including all fields used in the kernel analysis, is split into three downloadable records on Zenodo:<br>- Warming run (years 0001-0279)<br>- Cooling run part 1 (years 0001-0266)<br>- Cooling run part 2 (years 0267-0514)<br>Links to the three records can be found at https://eisenman-group.github.io</p> <p><br>Please contact Ian Eisenman (eisenman@ucsd.edu) for model output fields not included in this collection.</p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 8 . The Comparision of Run Times
<p>That is distinct that dynamic mutation rate or reduction idea for mutation operator is more<br> better of fixed rate. In fact obtain to high accuracy is result of our idea for mutation operator.<br> The number of hidden layer neurone is important problem for NN. The natural selection by<br> GA help finding the number of hidden layer neurone and it progress on duration generations.<br> The structured model of GANN finds better answer than NN but with much run time in<br> simulation. The learning of GA is much better than NN with back propagation because BP is a<br> method based on gradient descend and local optimum is a serious risk for that.<br> We hope that the number of training samples is more accurate without error, the new<br> algorithm is better. Tests show that the combination of genetic algorithms and neural networks to an<br> acceptable level solves the problem of overfitting.</p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
Run 5 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 5 presented in the paper "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence" (http://adsabs.harvard.edu/abs/2013MNRAS.428..804S or https://doi.org/10.1093/mnras/sts071).</p>
Coarse-grained outputs from near-global aqua-planet control run with QOBS SST
<p>This is the coarse-grained CTRL NG-Aqua data described in the following two papers:</p> <p>Narenpitak, P., Bretherton, C. S. & Khairoutdinov, M. F. Cloud and circulation<br> feedbacks in a near-global aquaplanet cloud-resolving model: Cloud Feedbacks in<br> a Near-Global CRM. J. Adv. Model. Earth Syst. 9, 1069–1090 (2017).</p> <p>Bretherton, C. S. & Khairoutdinov, M. F. Convective self-aggregation feedbacks<br> in near-global cloud-resolving simulations of an aquaplanet. Journal of<br> Advances in Modeling Earth Systems 7, 1765–1787 (2015).</p> <p><br> The data are generated using the System for Atmospheric Modeling, and then<br> selected fields are averaged onto (160 km)^2 grid boxes for machine learning<br> purposes.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route
<p>During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.</p>
Tasks run in DarkSkies Project (Cities At Night - 2016)
<p>Results of the DarkSkies application hosted in Crowdcrafting (http://www.crowdcrafting.org) in the year 2016.</p>
Tasks run in DarkSkies Project (Cities At Night - 2015)
<p>Results of the DarkSkies application hosted in Crowdcrafting (http://www.crowdcrafting.org) in the year 2015.</p>
Fig. 13 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 13. Ballomma neethlingi gen. et sp. nov. A–B. Paratype, ♂ (NCA 76/1048) C. Holotype, ♀. A. Male palp, retrolateral view. B. As preceding, ventral view. C. Epigyne, ventral view. Scale bars: 0.2 mm.
Fig. 15 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 15. Predicted distribution of Ballomma gen. nov. calculated in DIVA-GIS on the base of all climatic data as default in the software. Black dots present known localities of Balloma gen. nov. Dark green areas have expected occurrence with the 5–95 percentile; pale green with 2.5–97.5 percentile.
Fig. 12 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 12. Ballomma neethlingi gen. et sp. nov. A–B. ♂ (NCA 76/1048) C. Holotype, ♀. A. Male palp, retrolateral view. B. As preceding, ventral view. C. Epigyne, ventral view. Scale bars: 0.5 mm.
Fig. 10 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 10. Ballomma legala gen. et sp. nov. Holotype, ♀. A. Epigyne, ventral view. B. As preceding, ventral view. C. Epigyne, digested, dorsal view. D. As preceding, dorsolateral view. Scale bars: 0.2 mm.
Fig. 11 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 11. Ballomma neethlingi gen. et sp. nov. A–C. Paratype, ♂ (NCA 76/1048) D–E. Holotype, ♀. A. Habitus, dorsal view. B. As preceding, ventral view. C. As preceding, detail. D. Habitus, dorsal view. E. As preceding, ventral view. Scale bars: 1 mm.
Fig. 9 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 9. Ballomma legala gen. et sp. nov. Holotype, ♀. A. Habitus, dorsal view. B. As preceding, ventral view. C. As preceding, frontal view. D. As preceding, lateral view. E. Mouth parts, ventral view. Scale bars: 1 mm.
Fig. 8 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 8. Ballomma haddadi gen. et sp. nov. A–B. Holotype, ♂. C–D. ♀♀ (MRAC 132641). A. Male palp, retrolateral view. B. As preceding, ventral view. C. Epigyne, ventral view. D. As preceding, other specimen. Scale bars: 0.5 mm.
Fig. 5 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 5. Ballomma erasmus gen. et sp. nov. Paratype, ♀. A. Epigyne, cleared, ventral view. B. As preceding, dorsal view. C. Epigyne, SEM, dorsal view. D. As preceding, detail. Scale bars: A–B = 0.2 mm; C = 0.1 mm; D = 50 mm.
Fig. 7 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 7. Ballomma haddadi gen. et sp. nov. A–B. Holotype, ♂. C. ♀ (MRAC 132641). A. Male palp, retrolateral view. B. As preceding, ventral view. C. Epigyne, ventral view. Scale bars: 0.5 mm.
Fig. 2 in Ballomma, a new Afrotropical genus in the Cryptothelinae (Araneae, Zodariidae): eyes on the run
Fig. 2. Ballomma erasmus gen. et sp. nov. A–C. Paratype, ♀. D. Holotype, ♂. A. Habitus, dorsal view. B. As preceding, ventral view. C. As preceding, lateral view. D. Habitus, lateral view. Scale bars: 1 mm.
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.