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.
76
datasets available to search
ShareScore release 0.9.0
Dataset results
76 results for “quality indicator”
Indicators of Contaminant Sources, PFAS, and Water Quality in Ellerbe Creek and New Hope Creek, NC (2019-2022)
Thousands of chemical contaminants are found in urban stream globally. This is a dataset of water quality measures of (1) compounds that are indicative of specific contaminant sources, (2) common water quality measures [trace metals, major ions, nutrients], and (3) PFAS. Sampling was conducted in Ellerbe Creek and New Hope Creek in the Durham and Orange counties of North Carolina. Biweekly and synoptic sampling was undertaken to explore spatial and temporal variation in water concentrations.
Dissolved Organic Carbon Concentration, Dissolved Organic Matter Optical Properties, and Water Quality Indicators in the Plum Island Estuary (PIE), Massachusetts, USA (2018-2023)
This is a data set of paired in situ measurements of water quality parameters, total suspended solids concentration, and concentration and optical properties (absorption coefficient spectra and fluorescence indices) of dissolved organic matter (DOM) collected between 2018 and 2023 in the Plum Island Estuary and nearshore waters. In situ water quality measurements (salinity, temperature, optical dissolved oxygen saturation, turbidity, and dissolved organic matter fluorescence) were collected with a water quality sonde from the surface (top 1 m of water column), along with corresponding samples that were processed and analyzed in the lab for dissolved organic carbon (DOC) concentration, chromophoric DOM (CDOM), absorption coefficient spectra, DOM excitation-emission matrix (EEM) fluorescence, and total suspended sediment (TSS) concentration. The data were used in multiple studies (see manuscripts listed below) focusing on the dynamics of DOC and CDOM in the Plum Island Estuary.
The compiled 8-year dataset (2012-2019) consisting of weekly river water quality indicators (CODMn, DO, NH3-N and PH ) in majors 10 sub-basin of Yangtze river based on imputation of machine learning
<p>Water quality is significantly affected by global climate change and human activities, with diverse critical factors shaping its state in rivers and lakes. In the study, we utilized four indicators to characterize water quality: the physical water quality parameters included dissolved oxygen (DO, mg/L) and PH, while the chemical water quality parameters encompassed chemical oxygen demand (CODMn, mg/L) and ammonia nitrogen (NH3-N, mg/L). This study establishes weekly water quality models for typical 10 sub-basins along the Yangtze River using machine learning methods, which incorporate the impacts of hydro-meteorological and anthropogenic factors.These 10 sub-basins represent the principal tributaries of the Yangtze River basin and include Dongting Lake, the upper Han River, the lower Han River, the Jialing River, the Jinsha River, the Li River, the Min River, Poyang Lake, the Xiang River, and the Yuan River. This data collection was performed by National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/index_1.shtml). The water quality indicators discussed in this study are assessed in accordance with the national standard GB 3838-2002. Please refer to the paper for details.</p>
Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4 in Investigation Of Common Reed Regrowth On The Shores Of Recreational Lakes
Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4. The number of holidaymakers near parameters of common reeds on the shores of Bridvaisis, Gaustvinis and Gilius lakes (in the Bridvaisis, Gaustvinis and Gilius Lakes. The order from the bottom to the top) and differences boundary of the continuous line side indicates in morphological parameters of plants after the cases where p <0.01; dashed lines, where p <0.05. holidaymakers' visits.
Fig. 2 in Saproxylic weevils and edaphic beetles as indicators of environmental quality of relict forests in Piedmont lowlands (Coleoptera)
Fig. 2 – Map of Parco della Partecipanza, completely surrounded by agroecosystems. Map data: Google Earth, Maxar Technologies, used according to Google Earth Terms of Service.
Fig. 1 in Saproxylic weevils and edaphic beetles as indicators of environmental quality of relict forests in Piedmont lowlands (Coleoptera)
Fig. 1 – The collecting sites. Map data: Google Earth, Maxar Technologies, used according to Google Earth Terms of Service.
Fig. 5 in Saproxylic weevils and edaphic beetles as indicators of environmental quality of relict forests in Piedmont lowlands (Coleoptera)
Fig. 5 – Some of the weevils collected in the research. a, Kyklioacalles navieresi (Boheman, 1837); b, Kyklioacalles aubei (Boheman, 1837); c, Acalles echinatus (Germar, 1824); d, Echinodera hypocrita (Boheman, 1837). From Stüben (2014-2020), used with permission.
Towards an Assessment Rubric for EiPE Tasks in Secondary Education: Identifying Quality Indicators and Descriptors
<p>This study reports on reseach to investigating indicators and descriptors which characterize the quality of students’ code explanations to EiPE (Explain in Plain English) tasks in secondary education. </p> <p>First, a literature review was carried out to identify quality characteristics and descriptors that differentiate the quality of students’ responses in tertiary education. Then, the quality of upper-secondary students’ responses to a simple EiPE task was analyzed. The results of that analysis were used to refine our quality characteristics and descriptors, which were then translated into a prototypical rubric. In the final phase, the rubric was iteratively tested by two teachers and improved based on their feedback. The final rubric prototype can be used to provide feedback during formative assessment.</p> <p><br> This repository includes the EiPE comprehension task, the student responses, and the results from coding the responses:<br> - StudentResponses.pdf: task and anonymized solutions provided by students, translated to English<br> - Coding_data.xls: coding by two researchers until they came to an agreement (full set of 34 responses), and coding by Teacher1 on complete set, and coding by Teacher2 on the base-set (with duplicate responses omitted)</p>
Implementation of ATP and Microbial Indicator Testing for Hygiene Monitoring in a Tofu Production Facility Improves Product Quality and Hygienic Conditions of Food Contact Surfaces: A Case Study
<p>This is the code and associated data that was used to generate conclusions for the following manuscript published in Applied and Environmental Microbiology:</p> <p>DOI: 10.1128/AEM.02278-20</p> <p>Implementation of ATP and Microbial Indicator Testing for Hygiene Monitoring in a Tofu Production Facility Improves Product Quality and Hygienic Conditions of Food Contact Surfaces: A Case Study</p> <p>Authors: Jonathan H. Sogin(a), Gabriela Lopez Velasco(b), Burcu Yordem(b), Cari K. Lingle(b), John M. David(b), Mario Cobo(a), Randy W. Worobo(a)</p> <p>(a)Department of Food Science, Cornell University, Ithaca, NY, USA</p> <p>(b)3M Company, St. Paul, MN, USA</p> <p>Address correspondence to Jonathan H. Sogin, jhs397@cornell.edu</p>
Dataset_Scoping review_Changes in quality of cancer care assessed through performance indicators
<p>Data on which the conclusions of the study "<em>Changes in the quality of cancer care as assessed through performance indicators during the first wave of the COVID-19 pandemic in 2020: a Scoping Review</em>" rely, namely: articles included in the Scoping Review and performance indicators extracted and collated.</p>
Quality of care and performance indicators of mental health supported accommodation services in England
<p class="MsoNormal"><span>This dataset includes data from Mental Health supporting accommodation services in England. It includes information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript published in Plos One: "Almeda, N., García-Alonso, C. R., Killaspy, H., Gutiérrez-Colosía, M. R., & Salvador-Carulla, L. (2022). The critical factor: The role of quality in the performance of supported accommodation services for complex mental illness in England. Plos One, 17(3), e0265319. https://doi.org/10.1371/journal.pone.0265319"</span></p> <p class="MsoNormal"><span>The research associated with the present data focused on developing an analytical process for assessing the performance of the Mental health (MH) supporting accommodation services from 14 different regions of England considering the effect of the quality-of-care indicators in the performance. For doing so every service was classified in Residential Care (move on and non-move on oriented), Supported Housing or Floating Outreach. Then, information about the quality-of-care was collected from each domain of the instrument QuIRC-SA. Finally, a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used.</span></p> <p class="MsoNormal"><span>The main results of the analyses pointed out that the incorporation of quality domains as variables (outputs) in DEA had a neutral-positive or positive global impact on the performance of MH-supported accommodation services.</span></p>
Рис. 1. Станции отбора проб зоопΛанктона в Варваринском воΔохраниΛище, 28.07.2022. in Zooplankton as an indicator of water quality in the Varvara Reservoir
Рис. 1. Станции отбора проб зоопΛанктона в Варваринском воΔохраниΛище, 28.07.2022.
Forest quality and land use intensity indicators
<b>Description: </b><p>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/81"><b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=22">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Canopy-based forest quality metrics</b> (Worksheet Canopy)</p><p>Dimensions: 213 rows by 6 columns</p><p>Description: Fractional canopy cover and leaf area index</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date images were collected (Field type: Date)</li><li><b>LAI</b>: Leaf area index, corrected for clumping of leaves at plot level (25 m x 25 m) (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation) (Field type: Numeric)</li></ul><br></li><li><p><b>Above-ground biomass</b> (Worksheet AGB)</p><p>Dimensions: 203 rows by 11 columns</p><p>Description: Was derived from DBH and Height of trees for individuals >= 10 cm DBH using five different algorithms on the raw data. We additionally binned heights of trees to account for uncertainties in tree height measurements and used multiple published equations combined with wood density estimates drawn from a distribution of wood density values that differs for unogged, logged and severely logged forest stands. We used oil palm specific equations for biomass estimations in oil palm plots. They will be identical estimates across the five algorithms used. Oil palms have a fundamentally different physical structure to forest trees, so we estimated AGB in oil palm plantations separately using the equation 〖AGB〗_palm= (0.3747*height*100+3.6334)/1000 (Thenkabail et al. 2004). See Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers RMet al. 2015, Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, ENVIRONMENTAL RESEARCH LETTERS, Vol: 10:044019. </p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date field plot data were collected (Field type: Date)</li><li><b>AGB_Saner</b>: AGB estimates developed for mixed-species forest stands in East Kalimantan, Indonesia (Field type: Numeric)</li><li><b>AGB_Chave_wet</b>: AGB estimates developed for wet forest (Field type: Numeric)</li><li><b>AGB_Chave_moist</b>: AGB estimates developed for moist forest (Field type: Numeric)</li><li><b>AGB_K09</b>: AGB estimates developed for logged over old growth forest in Malaysian Sabah (Field type: Numeric)</li><li><b>AGB_N10</b>: AGB estimate developed for old growth forest in Malaysia for forests 110 km south-east of Kuala Lumpur (Field type: Numeric)</li><li><b>AGB_Chave14</b>: AGB estimates developed for pantropical forest assuming a wood density of 0.64 (Field type: Numeric)</li><li><b>AGB_Chave14_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li><li><b>AGB_Chave14_Bin5_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li></ul><br></li><li><p><b>SAFE project forest quality scores</b> (Worksheet Quality)</p><p>Dimensions: 203 rows by 4 columns</p><p>Description: Visual assessment of forest disturbance</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date of assessment (Field type: Date)</li><li><b>ForestQuality</b>: SAFE Project forest quality scores (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>Caneye software analyses carried out using Caneye v6.3.8 in August/September 2013</b> (Worksheet LAI_Caneye)</p><p>Dimensions: 237 rows by 16 columns</p><p>Description: LAI, fcover and fAPAR estimates derived from hemispherical images or using Sunscan Delta T device (Cambridge) if applicable</p><p>Fields: </p><ul><li><b>Plotname</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date on which photographs were taken (Field type: Date)</li><li><b>HemiUp</b>: Number of sample points = number of pictures taken - upward looking fisheye pictures (Field type: Numeric)</li><li><b>LAI_eff_v6</b>: LAI effective estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_true_v6</b>: LAI true (accounting for vegetation clumping) estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_eff_v5</b>: LAI effective estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>LAI_true_v5</b>: LAI true (accounting for vegetation clumping) estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>ALAeffv5</b>: Effective average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>ALAtruev5</b>: True average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>Fap_meas_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>Fap_meas_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2010-07-01 to 2014-01-10</p><p><b>Latitudinal extent: </b>4.4245 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Figure 2 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 2. Classification of stations based on similarities of macroinvertebrate communities.
Figure 1 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 1. Study area and stations.
Figure 1 in Macroinvertebrate-based biotic indices for evaluating the water quality of Kargı Stream (Antalya, Turkey)
Figure 1. Study area and stations.
On the relation between technical debt indicators and quality criteria in Stack Overflow discussions
<p>This is the replication package for the paper "On the relation between technical debt indicators and quality criteria in Stack Overflow discussions". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering.This is the replication package for the paper "On the relation between technical debt indicators and quality criteria in Stack Overflow discussions". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering.</p>
Quality and Trackability of Author Indicated Software Citations (NEST Case Study)
<p>This dataset contains a randomly selected minimal sample of 471 NEST software citations, which were provided by authors and published on the NEST software page as a publication list. The software citations were analyzed on their quality and trackability.</p> <p>The data was collected in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Greenspace and bluespace quality indicators
<p>Data extracted from literature on green and blue space quality indicators. Protocol for data extraction found here:</p> <pre>https://doi.org/10.5281/zenodo.6594870</pre> <p>Funded by Rural and Environment Science and Analytical Services Division (JHI-C6-1)</p>
Effects of Tai Chi Chuan on Psychobiological Indicators of Anxiety and Sleep Quality in Young Adults
ClinicalTrials.gov study NCT01624168. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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.