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958 results for “Data quality”
Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
<p>Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes.</p> <p>Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance.</p> <p>Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network.</p> <p>Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.</p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
Simulated data for: The evolution of mating preferences for genetic attractiveness and quality in the presence of sensory bias
<p>This repository contains simulated datasets relating to the publication: </p> <p>Henshaw JM, Fromhage L, Jones AG (2022) The evolution of mating preferences for genetic attractiveness and quality in the presence of sensory bias. Proc. Natl. Acad. Sci. USA (doi:10.1073/pnas.2206262119)</p> <p>In this paper, we simulate the evolution of female preferences for multiple male ornament types (e.g., 'Fisherian', 'handicap' and 'indicator' ornaments) that differ in their associations with genes for attractiveness and for general 'quality' (operationalized as the ability to acquire resources). We allowed for ornaments to differ in their saliency to females. We also analyze the causal mechanisms generating sexual selection (e.g., 'good genes' and 'sexy sons') using causal inference. </p> <p>Datasets are organised into ZIP files named after the corresponding figure in the publication. For further information see the publication and the file README.txt in this repository.</p>
Data Repository for Peatland Organic Matter Quality Varies with Latitude as Confirmed from FTIR and Ramped Pyrolysis Oxidation
<p><strong>Data information:</strong></p> <p>This is the dataset for a study by Sparrow et al. comparing the organic matter quality of peat cores in four climates (tropical, subtropical, boreal, and polar) using multiple analytical approaches. Data for 16 samples (samples from 4 depths in each of the 4 cores) are presented in the study and all relevant and necessary data are published in this repository:</p> <ul> <li>14C content and d13C results for 1) bulk and 2) ramped pyrolysis oxidation splits and standard materials</li> <li>Elemental analysis results (%C, %N, C:N)</li> <li>FTIR spectra from 650-4000 cm-1</li> <li>Ramped pyrolysis oxidation run data (time, sample oven temperature, baseline-corrected CO2 concentration)</li> </ul> <p><strong>Sample information:</strong></p> <ul> <li>The Tropical peat samples were collected from a core obtained in November 2011, “MDM11-2A,” (4.3727°N, 114.3550°E) in the Ulu Mendaram Conservation Area in the Belait District of Brunei Darussalem, northwest Borneo.</li> <li>The Subtropical peat samples were collected from the “Lox3” core (26.597°N, 80.357°W) in October 2015 from the Loxahatchee National Wildlife Refuge, Florida.</li> <li>The Boreal peat samples were collected from the “T3F” core (47.5063°N, 93.4527°W) from S1 Bog in July 2012 from the Marcell Experimental Forest, near Grand Rapids, Minnesota.</li> <li>The Polar peat samples were collected from the “CPP” core (68.3531°N, 19.0473°E) in June 2012 from Stordalen Mire, a peat plateau underlain by discontinuous permafrost near Abisko, Sweden.</li> </ul>
A synchronized estimation of hourly ground-level concentrations of six criteria air pollutants in China using data from the first geostationary air-quality monitoring satellite
<p>This dataset provides the ground-level concentrations of six criteria air pollutants estimated from the first geostationary air quality monitoring satellite GEMS with a multi-output random forest model.</p>
Data and metadata of crop yield and quality derived from H2020 Diverfarming project
<p>Data and metadata of crop yield and quality from different cases studies and long terms from WP3 "Crop production and quality", derived from H2020 Diverfarming project. This workpackage provides robust and sound data about how diversified cropping systems with low-input practices and efficient use of resources have positive effects on crop production and quality, and so, farm yields and economic revenues for farmers is increased. This can increase trading productivity with high quality outputs, so improving the competitiveness of European agriculture in the global market. http://www.diverfarming.eu.</p>
Tutorial for the 2022 ACM SIGMOD Conference: Spatial Data Quality in the IoT Era: Management and Exploitation
<p>Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.</p>
Data: Water quality and the biodegradability of dissolved organic carbon in drained boreal peatland under different forest harvesting intensities
<p><span>This repository consists of three files, which contain ground water and ditch water quality data in drained forested peatlands and dissolved organic carbon biodegradation to carbon dioxide. Experiments and results are presented in:</span></p> <p><span>Palviainen M., Peltomaa E., Laurén A., Kinnunen N., Ojala A., Berninger F., Zhu X., Pumpanen J. 2022. Water quality and the biodegradability of dissolved organic carbon in drained boreal peatland under different forest harvesting intensities. Science of the Total Environment 806: 150919 <a href="https://doi.org/10.1016/j.scitotenv.2021.150919">https://doi.org/10.1016/j.scitotenv.2021.150919</a>.</span></p> <p><span> </span></p>
Data from: Trends in habitat quality and habitat degradation within terrestrial protected areas
<p>Please see the paper<strong> "Trends in habitat quality and habitat degradation within terrestrial protected areas"</strong> for the definition of habitat quality, habitat degradation and different threats.</p> <p>This dataset describes global habitat quality and degradation for 2020, with a resolution of 250 m. Values of habitat quality and total habitat degradation can be used directly. <strong>Of note, values of habitat degradation caused by each threat should be multiplied by the relative threat weights before use.</strong> The details are as follows:</p> <ol> <li>"quality_c_2020.tif" represents habitat quality.</li> <li>"deg_sum_c_2020.tif" represents total habitat degradation which is caused by all 5 threats.</li> <li>"deg_sum_c_NonIrriCrp_2020.tif" represents habitat degradation caused by non-irrigated cropland, and should be multiplied by 0.2145 before use.</li> <li>"deg_sum_c_IrriCrp_2020.tif" represents habitat degradation caused by irrigated cropland, and should be multiplied by 0.2292 before use.</li> <li>"deg_sum_c_CrpVeg_2020.tif" represents habitat degradation caused by mosaic cropland-vegetation, and should be multiplied by 0.1623 before use.</li> <li>"deg_sum_c_VegCrp_2020.tif" represents habitat degradation caused by mosaic vegetation-cropland, and should be multiplied by 0.0541 before use.</li> <li>"deg_sum_c_Urban_2020.tif" represents habitat degradation caused by urban areas, and should be multiplied by 0.3400 before use.</li> </ol> <p>Besides, "InputData_forR.zip" provides data on protected-area-level habitat quality and habitat degradation, which is for statistical analyses by R in the paper "Trends in habitat quality and habitat degradation within terrestrial protected areas".</p>
Data from: Understanding Test Convention Consistency as a Dimension of Test Quality
<div> <div>This archive provides additional data for the article "Understanding Test Convention Consistency</div> <div>as a Dimension of Test Quality" by Martin P. Robillard, Mathieu Nassif, and Muhammad Sohail,</div> <div>published in ACM Transactions on Software Engineering and Methodology.</div> </div>
Data from: Data for habitat quality or quantity? Niche marginality across 21 plants and animals suggests differential responses between highland and lowland species to past climatic changes
<p>Climatic changes can affect species distributions, population abundance, and evolution. Such organismal responses could be determined by the amount and quality of available habitats, which can vary independently. In this study, we assessed changes in habitat quantity and quality independently to generate explicit predictions of the species' responses to climatic changes between Last Glacial Maximum (LGM) and present day. We built ecological niche models and distribution models for 21 reptile, mammal, and plant taxa from the Baja California peninsula inhabiting lowland or highland environments. Geological data suggests the CCSM global circulation model is a better representation of LGM climate for the Baja California peninsula. Significant niche divergence was detected for all clades within species, along with significant differences in the niche breadth and area of distribution between northern and southern clades. Most clades showed a reduction in distribution area towards LGM. Further, niche marginality (used as a measure of habitat quality) was higher during LGM for most clades, except for northern highland species. Our results suggest that changes in habitat quantity and quality can affect organismal response independently. This allows the prediction of genomic signatures associated with changes in effective population size and selection pressure that could be explicitly tested to support our models.</p>
Fig. 3 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 3: Workflow of the quality control procedure and quality flag (QF) attribution.
Smogseer: A Convolutional LSTM for forecasting air quality from Sentinel-5P data
<h1><strong>Training and checkpoint datasets </strong></h1> <h2><a href="#h_1501865622611722161313186" target="_blank" rel="noopener">S5PL2_5D.nc</a></h2> <p>This is the Sentinel-5P traning dataset for the Smogseer ConvLSTM model. The dataset was created using the xcube Sentinel Hub data store from the <a href="https://deepesdl.readthedocs.io/en/latest/datasets/datastores/#xcube-sentinel-hub-data-store">Deep Earth System Data Lab</a>.</p> <p>The dataset has the following characteristics:</p> <ul> <li>bbox= [68.137207,24.886436,84.836426,34.379713] #WGS84 // lon,lat,lon,lat</li> <li>res = (bbox[2]-bbox[0])/512 # ~3629m</li> <li>date_range = ['2019-01-01', '2023-12-31']</li> <li> <div>timesteps = '5D'</div> </li> </ul> <h2><a href="#h_4062378263291722161323566">X_val.npy</a></h2> <p>Validation feature data with shape: (74, 1, 291, 512, 6)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_9861938573981722161330905">Y_val.npy</a></h2> <p>Validation target data with shape: (74, 1, 291, 512, 1)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_321677095651722161353847">smogseer50.keras</a></h2> <p>Model weights for training the ConvLSTM with 50 epochs.</p> <h2><a href="#h_5931467847131722161401921">smogseer100.keras</a></h2> <p>Model weights for training the ConvLSTM with 10 epochs.</p>
NINFA_CS1_Quality data_'t Klooster
<p>Public data provided by Vitens from groundwater samples. Microbiological and chemical composition of a monitoring samples of different wells at "Pb. Hengelo `t Klooster" location. Data gathered from 2017 to 2022. </p>
Optimizing Quality-Aware Big Data Applications in the Cloud — Supporting Data
<p>Supporting experimental data for the paper "Optimizing Quality-Aware Big Data Applications in the Cloud", submitted to IEEE TCC.</p>
Data and code for "Clamp-tapering increases the quality factor of stressed nanobeams"
<p>Data presented (ringdown traces and Q factor calculations) with codes used to analyze the data and produce the figures for the results in "Clamp-tapering increases the quality factor of stressed nanobeams".</p>
Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors
<p>Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.</p>
Urban stormwater pond water quality and vegetation cover data
<p>These data include measurements of water physical and chemical parameters and littoral vegetation measurements in urban stormwater ponds located in three different urban land use types in Orlando, Florida, USA.</p>
PERCEIVE: WP2: Evaluation of EU citizens' perception of the EU project: Task2.1: Data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables</p> <p>2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)</p> <p>3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)</p> <p>4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)</p> <p>5. codebook for all regional data</p>
Stream water quality data in Tianmu Lake watershed
<p><span>Water samples were gathered from a total of 30 sites across the Tianmu Lake Watershed from July 2020 to September 2022. </span><span>Sampling was conducted approximately monthly throughout the study period. </span></p>
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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.