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.
9,882
datasets available to search
ShareScore release 0.9.0
Dataset results
9,882 results for “Scale”
Рис. 1–10. КопуΛятивные органы и ΔетаΛи строения паΛьп самцов Haplodrassus alexeevi sp. n. (1, 3, 4, 8), H. umbratilis (2, 5, 6, 9) и H. soerenseni (7, 10). 1–2 – паΛьпа, вентраΛьно; 3, 5, 7 – буΛьбус, вентраΛьно; 4, 6 – буΛьбус, вентро-ΛатераΛьно; 8–10 – отросток гоΛени паΛьпы. Масштабные Λинейки 0.2 мм. Figs 1–10. Copulatory organs and details of structure of male palps of Haplodrassus alexeevi sp. n. (1, 3, 4, 8), H. umbratilis (2, 5, 6, 9) and H. soerenseni (7, 10). 1–2 – palp, ventral view; 3, 5, 7 – bulbus, ventral view; 4, 6 – bulbus, ventro-lateral view; 8–10 – tibial apophysis. Scale bars 0.2 mm. in A new species of spiders of the genus Haplodrassus Chamberlin, 1922 (Aranei: Gnaphosidae) from the Northern Pre-Caspian area
Рис. 1–10. КопуΛятивные органы и ΔетаΛи строения паΛьп самцов Haplodrassus alexeevi sp. n. (1, 3, 4, 8), H. umbratilis (2, 5, 6, 9) и H. soerenseni (7, 10). 1–2 – паΛьпа, вентраΛьно; 3, 5, 7 – буΛьбус, вентраΛьно; 4, 6 – буΛьбус, вентро-ΛатераΛьно; 8–10 – отросток гоΛени паΛьпы. Масштабные Λинейки 0.2 мм. Figs 1–10. Copulatory organs and details of structure of male palps of Haplodrassus alexeevi sp. n. (1, 3, 4, 8), H. umbratilis (2, 5, 6, 9) and H. soerenseni (7, 10). 1–2 – palp, ventral view; 3, 5, 7 – bulbus, ventral view; 4, 6 – bulbus, ventro-lateral view; 8–10 – tibial apophysis. Scale bars 0.2 mm.
Recurring bleaching events disrupt the spatial properties of coral reef benthic communities across scales
<p>Marine heatwaves are causing recurring coral bleaching events on tropical reefs that are driving ecosystem change. Yet little is known about how bleaching and subsequent coral mortality impacts the spatial properties of tropical seascapes, such as patterns of organism spatial clustering and heterogeneity across scales. Changes in these spatial properties can offer insight into ecosystem recovery potential following disturbance. Here we repeatedly quantified coral reef benthic spatial properties around the circumference of an uninhabited tropical island in the central Pacific over a 9-year period that included a minor and severe marine heatwave. Benthic communities showed increased biotic homogenisation following both minor and mass bleaching, becoming more taxonomically similar with less diverse intra-island community composition. Hard coral cover, which was highly spatially clustered around the island prior to bleaching, became less spatially clustered following minor bleaching and was indiscernible from a random distribution across all scales (100–2000 m) following mass bleaching. Interestingly, the reduced degree of hard coral cover spatial clustering was already evident by the onset of mass bleaching and before any dramatic wholesale loss in island-mean coral cover occurred. Reductions in hard coral spatial clustering may therefore offer an early indication of the ecosystem becoming degraded prior to mass coral mortality. In contrast, the spatial clustering of competitive fleshy macroalgae remained unchanged through both bleaching events, while crustose coralline algae and fleshy turf algae became more spatially clustered at larger scales (200–700 m) following mass bleaching. Overall, benthic community spatial patterning became less predictable following bleaching and was no longer reflective of gradients in long-term environmental drivers that typically structure these remote reefs. Our findings provide novel insights into how climate-driven marine heatwaves can impact the spatial properties of coral reef communities over multiple scales.</p>
Dataset for "Large scale patterns and drivers of the diving behavior of large pelagic predators"
<p>This dataset comprises 694 independent diving depth estimates of large pelagic predators extracted from 101 tagging studies. For both daytime and nighttime observations, two unambiguous quantities were reported: 1) the preferred diving depth (D<sub>pref</sub>), representative of the approximate depth at which individuals spend most of the time, or in other words a mean representative depth; and (2) the preferred diving depth range (ΔD<sub>pref</sub>), representative of the portion of the water column where individuals are most commonly recorded by the tags, or, in other words, the vertical range over which they are most commonly observed. Alongside the diving depth data, three additional types of information were extracted to co-locate diving depth observations with environmental variables, and to account for potential ontogenetic behavioral effects. These additional pieces of information include the location and period of the observations, and a representative size of the tagged individual or group of individuals. The extraction process was replicated by two separate analysts to ensure accuracy and reliability. For further details on the dataset and extraction procedure, refer to the manuscript "<em>Large scale patterns and drivers of the diving behavior of large pelagic predators"</em>.</p>
Figure 3. A in A Low-Cost Trap to Monitor Parasitism of Macadamia Felted Coccid (Hemiptera: Eriococcidae) and Other Scale Insects
Figure 3. A. Encarsia lounsburyi parasitizing an MFC adult. B. A circular parasitoid emergence hole on an adult male MFC host, indicating parasitism.
Figure 2 in A Low-Cost Trap to Monitor Parasitism of Macadamia Felted Coccid (Hemiptera: Eriococcidae) and Other Scale Insects
Figure 2. The parasitoid emergence trap installed on a macadamia tree branch infested with macadamia felted coccid.
Figure 1. A. A low-cost 100 in A Low-Cost Trap to Monitor Parasitism of Macadamia Felted Coccid (Hemiptera: Eriococcidae) and Other Scale Insects
Figure 1. A. A low-cost 100 ml PET bottle can be used to construct a parasitoid emer- gence trap to study parasitism of MFC in the field. B. Basic components of the parasitoid emergence trap showing the PET bottle with modifications allowing for attachment to the tree branch and insertion of a glass collection vial. C. A fully assembled trap.
Figures 2–5 in Lobate Lac Scale (Paratachardina pseudolobata Kondo and Gullan) Invades from Oahu into Neighbor Islands: Statewide Survey in Hawaii
Figures 2–5. Lobate lac scale (LLS) sites surveyed. (2) Hawaii. LLS in Hilo was detected by HDOA staff (personal communication, 2017). (3) Maui. (4) Kauai. (5a) Molokai. (5b) Lanai. Color code for survey sites in Figures 2–5: Blue: no LLS detected. Yellow: LLS detected, but no longer detected in later surveys. Red: LLS detected and established.
Figure 1 in Lobate Lac Scale (Paratachardina pseudolobata Kondo and Gullan) Invades from Oahu into Neighbor Islands: Statewide Survey in Hawaii
Figure 1. Mature female lobate lac scale (x-shaped, deep maroon color) and crawlers (red color) (1a); sooty mold formation on twigs and leaves (1b).
Annotation Data: Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology
<p><strong>Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology</strong></p> <p>Genome annotation data</p> <p><strong>Genome Browser:</strong> <a href="https://plantgenomics.ncc.unesp.br/gen.php?id=Asub">https://plantgenomics.ncc.unesp.br/gen.php?id=Asub</a></p>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Fig. 1 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 1. Camera-trap image of mainland serow (Capricornis sumatraensis) from the lowlands of the Pasoh Forest Reserve in Peninsular Malaysia at an elevation of ~100 m.
Fig. 4 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 4. Regional-scale relationships between serow captures and covariates. Displayed are the variables within the top-performing multivariate model as assessed by lower AICc scores. All covariates are averaged at a 20-km radius around the study area.
Fig. 5 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 5. The relationships between serow predicted abundance and habitat variables at the local scale from Royle-Nichols hierarchical models. Oil palm, roughness and Human Footprint Index were in the top performing multivariate model.
Fig. 3 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 3. Presence of the serow within its Southeast Asian range. Panel a) shows the IUCN Red List range extent of occurrence (EOO; shaded orange), and the occurrence records coloured by the data source. Panel b) shows the jackknife-based assessment of variable importance. The blue bars showing the explanatory power in the model using only the denoted variable, while the teal bars show the predictive power of the full model without the denoted variable, highlighting whether the variable captures unique information. Panel c) shows the probability of presence of the serow from Maxent modelling mapped within the Southeast Asian region covered by this study. Panel d) shows the forest cover in 2015 that is potentially occupied within the EOO. Panel e) is the Maxent probability of presence of serow inside the remaining forested areas within Southeast Asia. Original artwork courtesy of Tamzin Barber (https://www.talkinganimals.com.au/).
A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
<p>Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks.</p> <p><br>If you use our dataset in your research, we kindly request that you cite the following paper:</p> <p>K. Kousias <em>et al</em>., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in <em>IEEE Communications Magazine</em>, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.</p>
Artifact for MobiCom'23: Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility
<p>This dataset contains the anonymized failure data collected from our physical and device farms over a three-month period. The failure data involves 5,918 physical devices as well as 5,918 virtualized devices running on ARM commodity servers.</p> <p>For more details, please visit our website (<a href="https://android-emulation-testing.github.io/">Android-Emulation-Testing.github.io</a>) or read our paper:</p> <ul> <li>[MobiCom'23] Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility</li> </ul> <p>If you use our dataset in your work, please reference it using:</p> <pre><code>@inproceedings {lin2022virtual, author = {Lin, Hao and Qiu, Jiaxing and Wang, Hongyi and Li, Zhenhua and Gong, Liangyi and Gao, Di and Liu, Yunhao and Qian, Feng and Zhang, Zhao and Yang, Ping and Xu, Tianyin}, title = {{Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility}}, booktitle = {The 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'23)}, year = {2023}, publisher = {ACM} }</code></pre> <p> </p> <p> </p>
Data and code for: Wildfire activity in northern Rocky Mountain subalpine forests still within millennial-scale range of variability
<p>Increasing area burned across western North America raises questions about the precedence and magnitude of changes in fire activity, relative to the historical range of variability (HRV) that ecosystems experienced over recent centuries and millennia. Paleoecological records of past fire occurrence provide context for contemporary changes in ecosystems characterized by infrequent, high-severity fire regimes. Here we present a network of 12 fire-history records derived from macroscopic charcoal preserved in sediments of small subalpine lakes within a c. 10,000 km<sup>2</sup> landscape in the U.S. northern Rocky Mountains (Northern Rockies). We used this network to characterize landscape-scale burning over the past 2500 yr and evaluate the precedence of widespread regional burning experienced in the early 20<sup>th</sup> and 21<sup>st </sup>centuries. We further compare the Northern Rockies fire history to a previously published network of fire-history records in the Southern Rockies. In Northern Rockies subalpine forests, widespread fire activity was strongly linked to seasonal climate conditions, in contemporary, historical, and paleo records. The average estimated fire rotation period (FRP) over the past 2500 years was 164 yr (HRV: 127-225 yr), while the contemporary FRP from 1900-2021 CE was 215 yr. Thus, extensive regional burning in the early 20<sup>th</sup> century (e.g., 1910 CE) and in recent decades was within the HRV of recent millennia. Results from the Northern Rockies contrast with the Southern Rockies, which burned with less frequency on average over the past 2500 yr, and where 21<sup>st</sup>-century burning has exceeded the HRV. Our results support expectations that Northern Rockies fire activity will continue to increase with climatic warming, surpassing historical burning if more than one exceptional fire year akin to 1910 occurs within the next several decades. The societal and ecological consequences of climatic warming in subalpine forests will depend, in large part, on the magnitude of fire-regime changes relative to the past.</p>
Pore-scale fluid dynamics resolved in pressure fluctuations at the Darcy scale
<p>Pressure data from the 5 experiments described in Spurin et al. Pore-scale fluid dynamics resolved in pressure fluctuations at the Darcy scale. <em>GRL, 2023. </em></p> <p>There are 2 data sets from the pore-scale observations with either gas or oil injected. There are 3 data sets from the core-scale observations: 1 with oil injected, and 2 with gas injected. The 2 gas experiments were conducted in the same sample, just with the flow direction reversed. </p> <p>This upload also includes the code to perform the continuous wavelet transform on the pressure data. </p>
Large scale seabird community stucture along oceanographic gradients in the Scotia Sea and northern Antarctic Peninsula
<p>Dataset and associated code for publication submitted to Frontiers in Marine Science. The data represent strip transect data collection efforts of seabirds aboard 2 tourist ships during the 2019-2020 Antarctic summer season throughout the Scotia Sea and Antarctic Peninsula. Data were analysed using R.</p>
Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems
<p>Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x)</p> <p>The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the <strong>R_codes</strong> folder following this sequence:</p> <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> <p>and using the data available in the <strong>Input_data</strong> folder.</p> <p>The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x</p> <p>However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set.</p> <p>A protocol for requesting access to the LEPAS data sets can be found here:<br> https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas</p> <p>Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: hood.211@osu.edu</p> <p>Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05.</p> <p>I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.</p>
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.