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151 results for “low quality”
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report
<p>The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): <em>Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007</em>–<em>2011: Final Data Report</em>, Kgs. Lyngby: National Museum of Denmark, 122 pp.</p> <p>The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report. </p> <p>The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted.</p> <p>For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).</p>
IMPACT OF US BROWN SWISS GENETICS ON MILK QUALITY FROM LOW-INPUT HERDS IN SWITZERLAND: INTERACTIONS WITH SEASON
<p>This study aimed to investigate the effect of, and interactions between, US Brown Swiss genetics and season on milk yield, basic composition and fatty acid profiles, from cows on low-input farms in Switzerland. Milk samples (n=1,976) were collected from 1,220 crossbreed cows with differing proportions of BS, Braunvieh and Original Braunvieh genetics on 40 farms during winter-indoor and summer-grazing seasons. Cows with more Brown Swiss genetics produced more milk in winter but not in summer, possibly because of underfeeding high-yielding cows on low-input pasture-based diets. Cows with more Original Braunvieh genetics produced milk with higher concentrations of (i) nutritionally desirable <em>trans</em>-9 palmitoleic, eicosapentaenoic and docosapentaenoic acids, throughout the year, and (ii) vaccenic and α-linolenic acids, total omega-3 fatty acids concentrations and a higher omega-3/omega-6 ratio during the summer-grazing period only. This suggests that overall milk quality could be improved by re-focusing breeding strategies on the cows’ ability to respond to local dietary environments and seasonal changes in feeding regimes.</p>
Cognition-mediated Evolution of Low-Quality Floral Nectars
<p>Data from virtual selection experiments, in which real or virtual nectar-feeding bats visited artificial or virtual flowers, exerting selection on their nectar traits.</p> <p> </p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Dataset for: IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost commodity Particulate Matter (PM) sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide-Area Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.</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>
SensEURCity: A multi-city air quality dataset collected using networks of open low-cost sensor systems
<p>We provide a unique curated dataset of urban air quality measurements acquired using dense networks of low-cost sensor systems in three European cities for the years 2020 and 2021. The dataset includes the raw sensor data of quality-controlled sensor networks along with co-located reference data sets. Sensor data are collected using the AirSensEUR sensor system, including sensors to monitor NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2, and meteorological parameters. In total, 85 sensor systems were deployed throughout the years 2020 and 2021 in three European cities (Antwerp , Oslo and Zagreb), resulting in a dataset comprising different meteorological and ambient conditions. The main data collection included two co-location campaigns in different seasons at an air quality monitoring station in each city and a deployment at different locations in each city (including also locations at other air quality monitoring stations). The dataset consists of data files with sensor and reference data, and metadata files with description of locations, deployment dates and description of sensors and reference instruments. </p>
Data Sets: An Assessment of Water Trusts: Drinking Water Quality and Provision in Six Low-income, Peri-urban Communities of Lusaka, Zambia
<p>Data set associated with the manuscript published in GeoHealth titled An Assessment of Water Trusts: Drinking Water Quality and Provision in Six Low-income, Peri-urban Communities of Lusaka, Zambia. This includes bacterial, nitrate, specific conductance, and Water Trust survey data collected from Lusaka, Zambia in 2013, 2014, 2016, an 2019.</p>
Low toxicity crop fungicide (Fenbuconazole) impacts reproductive male quality signals leading to a reduction of mating success in a wild solitary bee
<p>Recent reports on bee health suggest that sub-lethal doses of pesticides have negative effects on wild bee reproduction and ultimately on their population growth.</p> <p>Females of the solitary horned mason bee, Osmia cornuta, evaluate thoracic vibrations and odours of males to assess male quality. When certain criteria are met, the female accepts the male and copulates. However, these signals were found to be modified by sub-lethal doses of pesticides in other hymenopterans. Here, we tested whether sub-lethal doses of a commonly used fungicide (Fenbuconazole) impact male quality signals and mating success in O. cornuta.</p> <p>Males exposed to Fenbuconazole exhibited reduced thoracic vibrations and an altered cuticular hydrocarbon profile compared to the control bees. Moreover, males exposed to the fungicide were less successful in mating than control males.</p> <p>Synthesis and applications: Our results indicate that a low toxicity fungicide can negatively affect male reproductive success by altering behavioural and chemical cues. This could explain the decreasing pollinator populations in a pesticide-polluted environment. This study highlights the need for a more comprehensive approach, including behaviour and chemical cues, when testing new pesticides and a more cautionary approach to the pesticides already used on crops.</p>
EFFECTIVENESS OF PELVIC FLOOR EXERCISE TO PREVENT LARS (LOW ANTERIOR RESECTION SYNDROME) AFTER MINI-INVASIVE LOW ANTERIOR RESECTION IN PATIENTS WITH RECTAL CANCER. Adherence to prescribed home exercise. Quality of life after low anterior resection.
<p><span>Advances in the surgical treatment of rectal diseases lead to better oncological results, a higher chance of preserving the sphincters, and thus a lower number of permanent stomas. However, the preserved anus does not always have to perform its original function fully. All patients after a low anterior resection are at risk of developing functional disorders - low anterior resection syndrome (LARS). The prevalence of LARS ranges from 41-80% and is a significant factor in reducing the quality of life. </span><span>According to the available data, it is possible to prevent LARS through postoperative pelvic floor exercises, however, relevant studies are missing. </span><span>The pelvic floor is a ligament-muscle system that provides dynamic support for the organ systems located in the small pelvis. </span><span>Strengthening the muscles can serve as a follow-up treatment after surgical procedures including prevention of LARS.</span></p> <p><span>The main goal of this randomized study will be to determine the effectiveness of pelvic floor exercises on the incidence or severity of LARS in patients after mini-invasive rectal resection. Secondary goal is to identify the adherence of patients to prescribed home exercise and quality of life. </span></p> <p><span>Researchers will compare the group of patients with pelvic floor exercises to those without. Under the professional guidance of a physiotherapist, participants will be educated the day before surgery and in the first 4 postoperative days to exercise the pelvic floor exercise and continue at home for a month. </span><span>The resulting knowledge will have a fundamental impact on clinical practice and patient management.</span></p>
Dataset for the article "Biomedical Publishing in Russia: How big is low-quality papers problem?"
<p>The dataset is used in the article "Biomedical Publishing in Russia: How big is low-quality papers problem?" submitted to the Journal of the Medical Library Association : JMLA.</p> <p><br>The dataset contains the list of Russian and international biomedical journals along with their ISSNs, and eISSNs with attached thematic categories from Web of Science and SJR. For each journal-year pair, the number of publications is calculated. The time frame of analysis is 2010-2020.</p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Sugar and nitrogen digestive processing does not explain the specialized relationship between euphonias and low quality fruits
<p>In the Neotropical region, euphonias (Euphonia spp., Fringillidae) are the quintessential example of specialized bird frugivores, making the bulk of feeding visits to certain mistletoes (Phoradendron spp., Santalaceae) and epiphytes in the genus Rhipsalis (Cactaceae), whose fruits have high water and low sugar and protein concentrations. Surprisingly, a mechanistic explanation for such specialized, otherwise rare, relationships is lacking. Using captive birds and artificial diets, we contrasted euphonias with frugivorous tanagers in the genus Thraupis (Thraupidae), which rarely eats Rhipsalis fruits, to test the hypothesis that the digestive capacity of euphonias entails them to exploit such low-energy fruits. We expected that compensatory feeding in response to decreasing energy density would occur only in euphonias, whose higher reliance on fruits would entail a lower nitrogen requirement than the tanagers. Euphonias and tanagers were both able to compensate energy intake as sugar density decreased, and both species had the same mass-corrected energy intake at any given sugar concentration. Similarly, euphonias and tanagers did not differ in mass-corrected maintenance nitrogen requirement. Therefore, the physiological traits we investigated do not explain euphonia´s specialization on Rhipsalis fruits. The fast rates of fruit passage typical of specialized avian frugivores as euphonias that entail the processing of a large volume of fruits, and the putative better abilities of such birds to deal with secondary compounds likely present in Rhipsalis fruits are other possible mechanisms that should be considered in future studies to unveil the mechanisms underlying the intriguing specialized relationships between euphonias and certain fruits.</p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. 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A Dataset Containing Tiny Vehicle Images Collected in Low Quality Imaging Conditions
<p>This dataset contains 4800 tiny and low resolution vehicle images collected in low lighting and different weather conditions. The vehicles in the images are grouped in six classes: Bike, Car, Juggernaut, Minibus, Pickup, and Truck. For each class, there are 800 vehicle images with 100 × 100 pixels and 96 dpi resolution.</p> <p>The peer-reviewed data descriptor for this dataset has been published in MDPI Sustainability - an open access journal, and can be accessed here: <a href="https://doi.org/10.3390/su152316292">https://doi.org/10.3390/su152316292</a>. Please cite this when using the dataset.</p>
Prospective cohort study of a community-based primary care program's effects on pharmacotherapy quality in low-income Peruvians with type 2 diabetes and hypertension
<p>A door-to-door survey was conducted to enumerate all household members by age and sex in a low-income community in Peru. 856 adults 35 years and older were eligible to participate in screening for type 2 diabetes and hypertension. 709 (83%) participated in screening. 130 (18.3%) were diagnosed with hypertension and/or type 2 diabetes of which 109 (84%) participated at program onset and 22 were added later from earlier non-participants in screening or program onset to form the cohort of 131 patients with diabetes and/or hypertension. The primary care program had components of the Chronic Care Model, community health workers, and freely accessible visits and medications. The program operated between September 2011 and May 2014, and consisted of two care periods (separated by a six-month hiatus), first a 10-month home-care period, then a 17-month clinic-care period. The dataset is two files corresponding to two exposures: the 27-month program overall (post- versus pre-) (N=262 observations, 131 pairs with patients as self-controls) and care period (clinic versus home), N=211 (109 home and 102 clinic observations, >131 because 80 patients participated in both care periods). Exposures were evaluated for their effects on guidelines-based pharmacotherapy standards: hypoglycemic and antihypertensive medications, low-dose aspirin, and first-line angiotensin converting enzyme inhibitor (ACEi) treatment of diabetes with elevated blood pressure.</p>
Low Carbohydrate Diet, Glycaemic Control and Quality of Life in Australian Adults With Type 1 Diabetes
ClinicalTrials.gov study NCT04213300. IPD Sharing: NO. Countries: 1. Publications: 1.
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.