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”
A 3D-1D model for the simulation of plant-scale chemical reactors
<p>This dataset is the base for a 3D-1D model that has been developed to simulate the methane dehydroaromatization (MDA) process in plant-scale catalytic reactors.</p> <p>The related publication can be found in: https://zenodo.org/record/6206209#.YhO4XZYo82w</p>
Recording fine-scale movement of ground beetles by two methods: Potentials and methodological pitfalls
<p><span><span>Movement trajectories are usually recorded as a sequence of discrete movement events described by two parameters: step length (distance) and turning angle (bearing). One of the most widespread methods to record the geocoordinates of each step is by a GPS device. Such devices have limited suitability for recording fine movements of species with low dispersal ability including flightless carabid beetles at small spatio-temporal scales. As an alternative, the distance-bearing approach can avoid the measurement error of GPS units since it uses directly measured distances and compass azimuths. As no quantification of measurement error between distance-bearing and GPS approaches exists so far, we generated artificial fine-scale trajectories and in addition radio-tracked living carabids in a temperate forest and recorded each movement step by both methods. Trajectories obtained from distance-bearing were compared to those obtained by a GPS device in terms of movement parameters. Consequently, both types of trajectories were segmented by state-switching modeling into two distinct movement stages typical for carabids: random walk and directed movement. We found that the measurement error of GPS compared to distance-bearing was 1.878 m (SEM = 0.181 m) for distances and 31.330° (SEM = 2.066°) for bearings. Moreover, these errors increased under dense forest canopy and rainy weather. Distance error did not change with increasing distance recorded by distance-bearing but bearings were significantly more sensitive to error at short distances. State-switching models showed only slight, not significant, differences in movement states between the two methods in favor of the random walk in the distance-bearing approach. However, the shape of the GPS-measured trajectories considerably differed from those recorded by distance-bearing caused especially by bearing error at short distances. Our study showed that distance-bearing could be more appropriate for recording movement steps not only of ground-dwelling beetles but also other small animals at fine spatio-temporal scales.</span></span></p>
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID
<p>Aviation flight records and maintenance labels. </p>
Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos
<p>Dehaze Dataset LSFD</p>
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
<p>An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China.</p>
FIGURE 8. Tegmen. Scale bar 1 in A new subspecies of Ropalopus (Ropalopus) ungaricus (Herbst, 1784) from southern Spain, Ropalopus (Ropalopus) ungaricus occidentalis ssp. nov. (Coleoptera: Cerambycidae)
FIGURE 8. Tegmen. Scale bar 1 mm.
Data from: Cultural transmission of tool use combined with habitat specializations leads to fine-scale genetic structure in bottlenose dolphins
[No abstract entered]
Data from: Effects of abiotic environmental factors and land use on the diversity of carrion-visiting silphid beetles (Coleoptera: Silphidae): a large scale carrion study
[No abstract entered]
Data from: Causes and consequences of fine-scale population structure in a critically endangered freshwater seal
[No abstract entered]
FIGURE 7 in Bambusaecoccus maolanensis, a new soft scale genus and species from China (Hemiptera: Coccomorpha: Coccidae)
FIGURE 7. Bambusaecoccus maolanensis Meng & Xing sp. n., slide-mounted macropterous adult male.
FIGURE 3 in Bambusaecoccus maolanensis, a new soft scale genus and species from China (Hemiptera: Coccomorpha: Coccidae)
FIGURE 3. Bambusaecoccus maolanensis Meng & Xing sp. n., slide-mounted adult female.
FIGURE 6 in Bambusaecoccus maolanensis, a new soft scale genus and species from China (Hemiptera: Coccomorpha: Coccidae)
FIGURE 6. Bambusaecoccus maolanensis Meng & Xing sp. n., macropterous male.
Filtered data from "A first look at sea-lavenders genomics – can genome wide SNP information tip the scales of controversy in the Limonium vulgare species complex?"
Filtered data from "A first look at sea-lavenders genomics – can genome wide SNP information tip the scales of controversy in the Limonium vulgare species complex?" Limonium_species.tar.xz <p>Inside this file you will find all required data and metadata to reproduce the analysis using the environment from <a href="https://gitlab.com/StuntsPT/Limonium_GBS_data_analyses">this gitlab repository</a>. This inlcues both a <code>VCF</code> file for SNP data and a <code>Phylip</code> file for phylogenetic analyses. This is the datset "species" from the paper.</p> Limonium_lvu_lma.tar.xz <p>Inside this file you will find all required data and metadata to reproduce the analysis using the environment from <a href="https://gitlab.com/StuntsPT/Limonium_GBS_data_analyses">this gitlab repository</a>. This inlcues both a <code>VCF</code> file for SNP data and a <code>Phylip</code> file for phylogenetic analyses. This is the dataset "lvu_lma" from the paper.</p>
OMPs concentration in lab-scale AnMBR
<p>The spreadsheet contains the organic micropollutants concentrations and removal efficiencies of different contaminants: iopromide (IPM), sodium diatrizoate (DTR), sulfamethoxazole (SMX), trimethoprim (TMP), iohexol (IHX), roxithromycin (RMX), sulfadiazine (SDI), metronidazole (MTZ), ampicillin (AMP), acyclovir (ACV), triclosan (TCL), diclofenac (DCF), bezafibrate (BZF), and naproxen (NPX). <br> Those pollutants were sampled from the inlet and outlet of a 5 L lab-scale anaerobic membrane bioreactor (AnMBR). </p>
Source and result datasets for ""Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS"
<p>These files are the LRZip [0] compressed datasets used in the research paper 'Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS' [1]. The code can be found on Github [2].</p> <p>ls 2021-12-22-10\:49\:40<br> total 21M<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:38 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 7.7M May 25 12:10 domainfile.log.new.gz<br> -rw-r--r-- 1 sneef sneef 17K May 24 20:53 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 13M May 24 20:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 125 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 27K May 24 20:52 server.log.new.gz</p> <p>ls 2021-12-22-15\:04\:21<br> total 11G<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:43 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 4.2G May 23 12:43 certstream.log.new.gz<br> -rw-r--r-- 1 sneef sneef 3.3M May 23 12:37 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 6.0G May 23 12:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 104 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 6.3M May 23 12:37 server.log.new.gz</p> <p><br> ls results_certstream<br> total 17G<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 19:26 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 22:54 certstream_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 22:39 certstream_analysis_skipped_domains.txt<br> -rw-r--r-- 1 sneef sneef 4.5G May 23 13:21 certstream_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 450 May 23 13:22 certstream_counted_unique_domains_otherlines.csv<br> -rw-r--r-- 1 sneef sneef 602M May 23 13:22 certstream_counted_unique_domains_skipped_domains.csv<br> -rw-r--r-- 1 sneef sneef 3.6G May 23 13:22 certstream_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 6.1K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 39K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt_errors.txt<br> -rw-r--r-- 1 sneef sneef 3.1M Jun 8 19:20 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 94 Jun 8 19:26 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 37K May 23 13:41 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 45M May 23 13:41 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 22M May 23 13:41 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 2.1K Jun 8 23:09 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 23:09 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 1.4K Jun 8 23:09 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 23:08 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 2.6K May 23 13:41 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 76M May 23 13:41 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 206M May 23 13:41 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 3.4G May 23 13:41 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 7.0M May 23 13:41 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 423K May 23 13:41 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 3.9G May 23 13:40 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 6.0K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 259 Jun 8 00:51 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 305K Jun 8 00:24 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 1.4M May 23 22:41 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 63K May 23 22:41 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 74 May 23 22:41 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 9.1K May 23 22:41 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 239 May 23 22:41 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.7M May 23 22:41 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 1.7K May 23 22:41 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 17K May 23 22:41 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 79 May 23 22:41 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 70M May 23 22:41 serverlog_structured_data_structued_data.json</p> <p> </p> <p>ls results_tranco1m<br> total 79M<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 17:03 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 850 Jun 8 22:38 domainfile_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 domainfile_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 19M May 25 12:28 domainfile_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 4.0K May 27 03:03 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 16K Jun 8 16:32 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 83 Jun 8 17:04 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 226 May 25 12:28 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 66K May 25 12:28 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 31K May 25 12:28 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 0 Jun 8 22:38 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 900 Jun 8 22:38 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 915 Jun 8 22:38 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 909 Jun 8 22:38 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 844 Jun 8 22:38 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 829 Jun 8 22:38 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 27 May 25 12:28 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 589K May 25 12:28 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 269K May 25 12:28 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 18M May 25 12:28 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 58K May 25 12:28 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.8K May 25 12:28 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 4.4K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 241 Jun 8 11:45 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 5.4K Jun 7 23:58 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 2.0K Jun 7 15:57 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 69 Jun 7 15:57 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.5K Jun 7 15:57 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 129K Jun 7 15:57 serverlog_structured_data_structued_data.json<code> </code></p> <p><br> <br> [0] https://github.com/ckolivas/lrzip<br> [1] TBD<br> [2] https://github.com/gehaxelt/sshfp-dns-measurement</p>
Multi-scale plasticity homogenization of Sn–3Ag-0.5Cu: From β-Sn micropillars to polycrystals with intermetallics
<p>Data bundle for "Multi-scale plasticity homogenization of Sn–3Ag-0.5Cu: From β-Sn micropillars to polycrystals with intermetallics" <a href="https://doi.org/10.1016/j.msea.2022.143876">6</a> <a href="https://doi.org/10.1016/j.msea.2022.143876">https://doi.org/10.1016/j.msea.2022.143876</a> / <a href="https://doi.org/10.48550/arXiv.2208.11453">https://doi.org/10.48550/arXiv.2208.11453</a> </p> <table summary="Additional metadata"> <tbody> <tr> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Supplementary material 3 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Maps of the individual potential FRES demand indicators
Supplementary material 2 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Table of the scaled Flood Regulating Ecosystem Services indicators and categories
Supplementary material 1 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Ratio of FRES supply and flood hazard
"A Lot Takes Place Digitally Now, so It Can Be Good to Train on It'': A Large-Scale Repeated Cross-Sectional Study on Recording Live-Streamed Educational Activities among Health, Social, and Education Students
<p>Research article</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.