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.
958
datasets available to search
ShareScore release 0.9.0
Dataset results
958 results for “Data quality”
Fig. 4 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 4: A) Schematic representation of a possible spike (V2) evaluated based on its previous (V1) and subsequent (V3) values of the profile. B) Examples of identified spikes in nitrate profiles at low (pMC 535, -5 m), medium (●M276, -5m) and high (○MC48, -2 m) concentrations. Spikes are in orange.
Fig. 1 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 1: Geographical position of sampling stations for LTER-MC (●), Si.Di.Mar (●) and TYR (●) datasets.
Fig. 2 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 2: Frequency distribution of individual parameters in the LTER-MC dataset before applying QC (TEMP: temperature, PSAL: salinity, NTRI: nitrites, PHOS: phosphates, AMON: ammonia, NTRA: nitrates, SLCA: silicates, CHLT: chlorophyll a, S: skewness and K: kurtosis).
Fig. 9 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 9: Flags (¡ QF0, pQF2, and ÚQF6) for surface nitrates (NO3) concentrations in the Si.Di.Mar. dataset.
Fig. 6 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 6: Colour-coded flags for temperature data in monthly vertical temperature profiles in the LTER-MC dataset.
Fig. 5 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 5: Chlorophyll a data for April at 10 m depth: comparison of a standard (on the left) and an adjusted box plot (Hubert & Vandervieren, 2008) at LTER-MC.
Fig. 7 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 7: Percentages of quality flags attributed through the QC procedure to the whole LTER-MC dataset (~84,000 data-points).
Soilcare data agricultural soil quality improvement
<p>Dataset that contains survey data on Spanish and UK respondents on agricultural soil quality protection and improvement</p>
FIGURE A5 in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE A5. Scatter plot displaying how beam shift, measured by the apparent growth (scale) of the shell in the final x-ray radiograph relative to the first x-ray radiograph, varies with (1) time of day the scan was performed, and (2) the overall scan time. We found that beam shift decreased throughout the day, likely due to the tube warming up as more scans are run. There was no correlation between overall scan time and the amount of beam shift.
FIGURE A1.2 in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE A1.2. The range of volume measurements for each exposure times when three scans were reconstructed using ten methods. The blue dot represents the simple surface determination used for all shells in this study. The error bars represent the data range for each exposure time.
FIGURE 4. A in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE 4. A video moving through slices of a pteropods shell scanned at 500 ms exposure time, with five xray radiographs averaged per view and an overall scan time of 75 minutes. There are streak artefacts perpendicular to the shell edge that are likely caused by beam hardening or shell movement during the scan. For video file, see https://palaeo-electronica.org/content/2020/ 2923-investigating-ct-scan-quality.
Marcellus shale water and air quality data
<p>Data summary for water and air quality of the Marcellus shale</p>
Aggregated frequencies of transcription initiations observed in FANTOM5 CAGE data on GRCh38, including alignments with low mapping qualities
<p><strong>Overview</strong></p> <p>Aligned reads of the FANTOM5 CAGE data have been used after filtering (ones with mapping quality less than 20 or percent identity less than 85% were discarded) for general purpose, resulting in the data set consisting of only the reads aligned with confidence. The filtering process made possible to interpret the data without ambiguity, however it also limited interpretation of paralogous or duplicated regions within the genome. Here all of the 5'-ends of the CAGE read alignments, including the ones with low mapping quality, were counted. The counts in the individual profiles were aggregated and summed up. </p> <p> </p> <p><strong>Special usage note</strong></p> <p>As noted above, this data derived from the alignments with low mapping qualities, as well as the ones with high mapping qualities. The result has to be examined very carefully: observations on the genome does not support transcription initiation with confidence, and even absence of such observation does not support silence of transcription with confidence. For example, file size on the forward strand is substantially larger than the one on the reverse strand, which is likely caused by an arbitrary preference of the alignment process. It does not mean transcription happens more frequently on the forward strand. Interpretation has to be made always in comparison with the standard data (BED files under http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v4/basic/ or bigWig files under http://fantom.gsc.riken.jp/5/datahub/hg38/reads/).</p> <p> </p> <p><strong>Data files</strong></p> <p>The resulting data files are formatted as bigWig (https://genome.ucsc.edu/FAQ/FAQformat.html#format6.1). '*.fwd.bw' and '*.rev.bw' represent forward and reverse strand on the genome, respectively. </p> <p> </p> <p><strong>Methods</strong></p> <p>The BAM files under http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v4/basic/ were subjected to 5'-end counting by bedtools v2.27.1 (https://github.com/arq5x/bedtools2), followed by conversion into bigWig with jksrc v357 (http://hgdownload.cse.ucsc.edu/admin/).</p> <p> </p>
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 1
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the first project year (2017), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area.<br> Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2017<br> - Finland: links to repositories of EO data<br> - Italy Trasimeno: sample of EO data 2017<br> - Lithuania Curonian Lagoon: sample of EO data 2017<br> - Netherlands Lake Markermeer: sample of EO data 2017<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: in situ data Loch Leven and Loch Lomond 2017<br> - UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017</p> <p>http://eomores-h2020.eu</p>
Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers
<p>Hydraulic and tracer data used in the Chillan case study presented in "Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers"</p>
Program data - quality improvement for CBFP
<p>This data is de-identified program data from a quality improvement project on community-based family planning in two districts in Uganda.</p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Water quality data with nitrate
<p>Water quality data in a drinking water distribution network (Helsinki and Vantaa, in Finland). This data includes all the samples that have nitrate analysis. Monochloramine is used in the distribution network as secondary disinfection chemical. The water originates from Lake Päijänne. The titles of the Excel file are in Finnish.</p> <p>Included are an Excel file, an abstract and a poster, where this data was partly utilized. The reference of the poster and abstract: <a href="https://research.aalto.fi/portal/pirjo.rantanen.html">Rantanen, P</a> 2015, <a href="https://research.aalto.fi/en/publications/nitrification-in-drinking-water-distribution-network-in-helsinki-and-vantaa(c7db018f-3225-4e9f-9c05-237533d3669a).html">Nitrification in drinking water distribution network in Helsinki and Vantaa</a>. in Finnish Conference of Environmental Sciences 12th May 2015, Jyväskylä. ed. / Elijah Ngumba; Tuula Tuhkanen; Matti Leppanen; Jaakko Mannio; Sanna Pynnonen. Jyväskylä : FCES, 2015. p. 51.</p>
Tomato 'de penjar' fruit quality data
<p><span>A collection of 44 varieties of ‘de penjar’ tomato from different origins throughout the Valencian Region, located on the Spanish eastern coast, were evaluated under two N fertilization conditions. The collection was composed of 39 local varieties and 5 commercial varieties of the BRESOV collection. Data from University of Valencia</span></p>
Santiago Laxopa Zapotec voice quality data
<p>This contains a csv file containing the acoustic measures that were generated from VoiceSauce. </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.