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.
7,515
datasets available to search
ShareScore release 0.7.1
Dataset results
7,515 results for “screenings”
S71 | CECSCREEN | HBM4EU CECscreen: Screening List for Chemicals of Emerging Concern Plus Metadata and Predicted Phase 1 Metabolites
<p>This is the collection associated with list S71 CECSCREEN HBM4EU CECscreen: Screening List for Chemicals of Emerging Concern Plus Metadata and Predicted Phase 1 Metabolites<strong> </strong>on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>CECScreen is part of the HBM4EU project (coord. UBA) > WP16 "emerging chemicals" (lead INRA, JP Antignac/L Debrauwer) > Task 16.1 (lead IRAS, J Vlanderen / R Vermeulen) > Main contributor (J Meijer) > Involved Partners (M Lamoree, T Hamers, S Hutinet, A, Covaci, C Huber, M Krauss, DI Walker, EL Schymanski). Further details in Meijer et al (2021) DOI: <a href="https://doi.org/10.1016/j.envint.2021.106511">10.1016/j.envint.2021.106511</a>. Dataset DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>.</p> <p>Update 23/7/2020 (v0.1.1): updated MetFrag files to remove elements causing errors (Os, Pd, Ag, Be). Update 8 Nov 2022 (v0.1.2) removed new lines in several synonyms as detected at BioHackEU22.</p>
Non-Targeted Screening of Organic Compounds in Environmental and Biological Matrices Related to Children's Environmental Exposure in South Florida, 2022-2024
This dataset provides a comprehensive list of chemicals relevant to children’s exposure from both dietary and non-dietary sources, across five environmental and biological matrices: drinking water (n = 206), food (n = 203), urine (n = 183), soil (n = 178), and household dust (n = 164). Samples were collected between May 2022 and June 2024 in Miami-Dade and Broward counties, Florida. A non-targeted screening approach using high-resolution mass spectrometry (HRMS) coupled with liquid chromatography was employed for analysis, with matrix-specific preparation methods: online solid-phase extraction (SPE) for water and urine, QuEChERS for food, and accelerated solvent extraction (ASE) for soil and dust. Analyses were conducted in full-scan mode under both positive and negative electrospray ionization to maximize compound detection coverage. Compound identification was performed using Compound Discoverer software, incorporating spectral and structural databases such as mzCloud, ChemSpider, ClassyFire, and MassList. Annotations were based on exact mass, mass error threshold (<5ppm), predicted molecular formula, retention time alignment, isotopic pattern fit, MS/MS spectral similarity, and match confidence levels derived from integrated spectral libraries and database scoring algorithms. Quality assurance was maintained through the use of quality control (QC) samples across all matrices and analytical batches. The integration of non-targeted analysis, matrix-optimized extraction, and rigorous QA/QC practices makes this dataset a valuable resource for environmental exposomics, chemical risk assessment, and evidence-based public health policy development.
Imputed Forest Composition Map for New England Screened by Species Range Boundaries 2001-2006
Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped contiguously over large areas. Nearest-neighbor imputation maps have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances results in species frequently mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and selecting field plots for imputation based on their similarity in feature-space, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We applied this approach to map 13m ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). The map itself is a .img raster file of FIA plot CN numbers. To access FIA data from this map, one has to link the mapcodes in this map to FIA data supplied by USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html). Due to plot confidentiality and integrity concerns, pixels containing FIA plots were always assigned to some other plot than the actual one found there.
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
Software and suspect database for: "A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences"
<p>This upload contains the pesticide suspect list aggregated among the laboratories of work package 16 of the HBM4EU (https://www.hbm4eu.eu) project for a large-scale pesticide suspect screening and the resolving search templates for each pesticide. Additionally, we provide the used software version of MetAlign applied in this screening.</p>
Datasets for practical model selection for prospective virtual screening
<p>This repository contains datasets for the manuscript "Practical model selection for prospective virtual screening":</p> <ul> <li><strong>pria_rmi_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets. The files also contain the associated continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation.</li> <li><strong>pria_rmi_pcba_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets as well as public PubChem BioAssay datasets. The files also contain the PriA-SSB and RMI-FANCM continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation. Missing values are left blank.</li> <li><strong>pria_prospective.csv.gz</strong>: A compressed file containing chemical screening data for the binary dataset <strong>PriA-SSB prospective</strong>. The file also contains the continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.</li> </ul> <p>If you use these data in a publication, please cite:</p> <p>Shengchao Liu<sup>+</sup>, Moayad Alnammi<sup>+</sup>, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. Practical Model Selection for Prospective Virtual Screening. Journal of Chemical Information and Modeling. 2018 <a href="https://doi.org/10.1021/acs.jcim.8b00363">doi:10.1021/acs.jcim.8b00363</a></p> <p>PubChem data were provided by the <a href="https://pubchem.ncbi.nlm.nih.gov/">PubChem database</a>. Follow the <a href="https://pubchemdocs.ncbi.nlm.nih.gov/citation-guidelines">PubChem citation guidelines</a> if you use the PubChem data. See <a href="https://doi.org/10.1177/2472555217712001">Voter et al. 2017</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>) for the PriA-SSB screening data and <a href="https://doi.org/10.1177/1087057116635503">Voter et al. 2016</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1159607">1159607</a>) for RMI-FANCM.</p> <p>Version 1.1.0 updates all of the data files. We standardized the SMILES in all files by generating canonical SMILES with RDKit version 2016.03.4. In addition, we removed 2845 chemicals from pria_prospective.csv.gz that were duplicates of compounds in pria_rmi_cv.tar.gz.</p>
NMR screen reveals the diverse structural landscape of a G- quadruplex library
<p>This is the NMR dataset for the manuscript '<span>NMR screen reveals the diverse structural landscape of a G-</span><br><span>quadruplex library</span>'</p> <p>Abstract</p> <p><span>G-quadruplexes are noncanonical nucleic acid structures</span><br><span>formed by stacked guanosine tetrads. Despite their functional and</span><br><span>structural diversity, a single consensus model is typically used to</span><br><span>describe</span><span> </span><span>sequences</span><span> </span><span>with</span><span> </span><span>the</span><span> </span><span>potential</span><span> </span><span>to</span><span> </span><span>form</span><span> </span><span>G-quadruplex</span><br><span>structures. We are interested in developing more specific sequence</span><br><span>models</span><span> </span><span>for</span><span> </span><span>G-quadruplexes.</span><span> </span><span>In</span><span> </span><span>previous</span><span> </span><span>work,</span><span> </span><span>we</span><span> </span><span>functionally</span><br><span>characterized each sequence in a 496-member library of variants of a</span><br><span>monomeric</span><span> </span><span>reference</span><span> </span><span>G-quadruplex</span><span> </span><span>for</span><span> </span><span>the</span><span> </span><span>ability</span><span> </span><span>to</span><span> </span><span>bind</span><span> </span><span>GTP,</span><br><span>promote a model peroxidase reaction, generate intrinsic fluorescence,</span><br><span>and to form multimers. Here we used NMR to obtain a broad overview</span><br><span>of the structural features of this library. After determining the</span><span> </span><span>1</span><span>H NMR</span><br><span>spectrum of each of these 496 sequences, spectra were sorted into</span><br><span>multiple classes, most</span><span> </span><span>of</span><span> </span><span>which could be rationalized based on</span><br><span>mutational patterns in the primary sequence. A more detailed screen</span><br><span>using representative sequences provided additional information about</span><br><span>spectral classes, and confirmed that the classes determined based on</span><br><span>analysis of</span><span> </span><span>1</span><span>H NMR spectra are correlated with functional categories</span><br><span>identified in previous studies. These results provide new insights into</span><br><span>the surprising structural diversity of this library. They also show how</span><br><span>NMR can be used to identify classes of sequences with distinct</span><br><span>mutational signatures and functions.</span></p> <p><span>Link to journal article: <a href="https://doi.org/10.1002/chem.202401437"><span>https://doi.org/10.1002/chem.202401437</span></a></span></p>
Phytochemical Screening, Antioxidant and Antimicrobial Activity of Fabric Coated with Catharanthus Roseus Ethanolic Flowers Extract
<p>The aim of the present study was to evaluate the free radical scavenging and antimicrobial activity of fabric coated of the Catharanthus Roseus. Ethanol flowers extract. Free radical scavenging was determined by using 1, 1-diphenyl-2-picrylhydrazyl (DPPH), Reducing power, Hydroxyl radical scavenging assay and antimicrobial activity of Staphylococcus aureus, Escherichia coli and standard drug of Streptomycin using disc diffusion method. This inhibition was observed with the individual extracts and when they were used in lower concentrations with ineffective antibiotics. The present investigation clearly indicates that the Catharanthus Roseus possesses antioxidant properties and serve as free radical inhibitors or scavengers, acting possibly as primary antioxidants.</p><p>Keywords</p><p>Catharanthus Roseus, Fabric coated, DPPH, Staphylococcus aureus Escherichia coli, Streptomycin, Antioxidant,</p>
Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions
<p>This Zenodo entry contains the supplementary material associated with the manuscript titled <em>Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99.</em> The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024.</p> <p>A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well. </p> <p>[1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." <em>Mendeley Data</em>, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.</p>
Rapid spectroscopy-based screening techniques for spices data
<p>The dataset from the analysis of spices with a rapid spectroscopy-based screening technique, Fourier transform-Raman (FT-Raman) spectroscopy. Measurements are taken for the authentication of spice (i.e. turmeric) using FT-Raman spectroscopy as part of WP3 (Task 3.1): Implementation of innovations in food authenticity. The dataset is generated to develop a method for the rapid detection of lead chromate in turmeric powder. Measurements (FT-Raman spectra) are averaged per sample and only the final average spectral data is provided in the Excel sheets. The data is useful for anyone working with spectral data and its use for the authentication of spices.</p> <p>Data underlying the publication: Real or fake yellow in the vibrant colour craze: Rapid detection of lead chromate in turmeric.</p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Cancer screening attendance rates in transgender and gender-diverse patients: a systematic review and meta-analysis
<p>Supplementary Data to support the findings of a systematic review investigating cancer screening rates in transgender and gender-diverse individuals.</p>
Dataset of the paper entitled methods for high-throughput screening of novel agents against the maize pest, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae)
<p>Title: Methods for high-throughput screening of novel agents against the maize pest, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae) </p> <p>Authors: Sri Ita Tarigan, Gyorgy Turoczi, Jozsef Kiss, Stefan Toepfer</p> <p>Abstract: <br>The western corn rootworm, <em>Diabrotica virgifera virgifera</em> (Coleoptera: Chrysomelidae), poses a significant threat to maize crops in North America and Europe, necessitating development of novel, effective, and less disruptive crop protection agents. With recent bans on key insecticides and concerns about overuse of remaining options, there is an urgent need for accessible and comparable screening methods. We propose comparative high-throughput screening methods against the eggs, larvae and adults of this pest, emphasizing the importance of suitable positive controls tailored to the specific bioassay types. We evaluated seven common insecticides (imidacloprid, clothianidin, acetamiprid, novaluron, cypermethrin, chlorpyrifos-methyl, spinosad) against eggs, larvae, and adults as potential positive controls for each of the proposed assay methods. Dipping assays with ready-to-hatch eggs revealed several ingredients to cause mortality; but imidacloprid might be most suitable as a positive control due to its robust dose-response in reducing egg hatching and causing mortality of hatching neonates. Larval bioassays using artificial diet overlay assays revealed mortality caused by all insecticides, with imidacloprid and acetamiprid exhibiting best dose-mortality response curves as well as sublethal effects. Adult bioassays using artificial diet-core overlay assays revealed mortality caused by all insecticides, with cypermethrin or acetamiprid exhibiting best dose-mortality response curves. The provided ED <sub>50</sub>, ED <sub>80</sub> values, and dose-response equations offer valuable insight for researchers in selecting appropriate positive controls for screening new crop protection agents or assessing resistance levels against different life stages of this pest.</p> <p>Data:</p> <p>The data file is related to the screening of commercial insecticides against eggs, first instar larvae (L1) and adults of the maize pest, <em>Diabrotica virgifera virgifera</em> using standard bioassays. We are proposing comparative high-throughput screening methods against the eggs, larvae and adults of this maize pest. This includes the crucial aspect of suitable positive controls tailored to the specific bioassay type. We evaluated seven common insecticides (imidacloprid, clothianidin, acetamiprid, novaluron, cypermethrin, chlorpyrifos-methyl, spinosad) against eggs, larvae, and adults as potential positive controls for each of the proposed assay method. To access effects and dose-responses of commonly used insecticides on eggs, we applied standard screening methods under controlled semi-sterile conditions.</p> <p>For egg bioassays, eggs were transferred to the 200 ml of treatments in the eppendorf tubes and then soaked for 1 hour. Then 20µl with 10 to 20 eggs were pipetted onto a filter paper in a petri dish (150 mm×25 mm). Then 100 µl of sterilized tap water was added for moisture. The pipette tip was replaced between treatments. The eggs been transferred were counted per filter paper and dish (15± 8). The eggs were then incubated in the dishes at 23-25<sup>0</sup>C for 7 days, when the experiment was terminated. Egg hatching, mortality of newly hatching larvae, and days until start of egg hatching were observed under stereo microscope and recorded. Data were collected at 1,3, 5 and 7 days after treatments.</p> <p>To assess the effect and dose-responses of commonly used insecticides on neonates of <em>D. v. virgifera</em>, we applied artificial diet-overlay bioassays under controlled semi-sterile conditions. Each insecticide was prepared in at least six concentrations. Each bioassay consisted of 3 to 6 polystyrene plates of 96 wells each (07-6096 of Biologix Ltd., USA, or Costar 3917 of Corning Inc., USA). Each well had a volume of 330 µl, with a diameter of 5 mm, a height of 10 mm, and a surface area of 0.34 cm². 190 µl of the diet were pipetted into each 330 µl well, filling each to approximately 2/3<sup>rd </sup>of its capacity. Plates containing the diet were left to dry in a laminar flow cabinet for 45 minutes and then stored overnight at temperatures ranging from 3 to 5°C. The following day, treatments were applied. This is, 17 µl of a treatment was applied to the 0.34 cm<sup>2</sup> diet surface reaching good coverage and therefore forcing the after-placed larvae to feed through (10 to 100 µl pipette Biohit TM Proline). Each treatment was applied to 8 wells per plate. Following application, the plates were allowed to dry for a duration of 1 to 1.5 hours and were subsequently cooled for 1 hour in a refrigerator set at temperatures between 23 to 25°C. Each well received one neonate larva, carefully placed on the diet surface using a fine artist brush. A vigorous and visibly healthy larva was selected, lifted from the end of the abdomen with the brush, maneuvered towards a well surface, and allowed to crawl off the brush onto the diet. To avoid systematic errors, larvae were not arranged in treatment column order but rather in a rectangular pattern. After every 12 individual larvae, the brush was cleaned using 70% ethanol followed by sterile tap water. The filled plate was sealed with an optically clear adhesive qPCR seal sheet (#AB-1170, Termo Scientific, USA, or #BS3017000, Bioleader, USA), enabling data assessments without the need to open the plate. Four to five holes were carefully made with fine 00-insect pins into the seal per well to facilitate aeration. The plates, housing the larvae, were then incubated in a dark, ventilated incubator at a temperature of 23-25 °C and a relative humidity of 50 to 90% for a period of 5 days. We assessed mortality and stunting larvae within 3 and 5 days. </p> <p>To access the effect and dose-responses of common insecticides on <em>D.v.virgifera</em> adults, artificial diet-overlay bioassays with different doses were performed under controlled, semi-sterile conditions. Each insecticide was prepared in at least six concentrations. Active ingredients as specified on the product labels underwent serial dilutions using sterile tap water. Sterilized tap water was used as untreated control. In detail, each bioassay consisted of 6 polystyrene plates of 6 wells each (Eppendorf® 0030720016). Each treatment was applied to 3 wells of each plate per bioassay. The adult diet for a bioassay had been prepared 1-7days before treatment and adult infestation. The diet was prepared under semi-sterile conditions. The diet was poured out to 5-6 sterile 11 mm Petri dishes. The plates with diet were allowed to dry for up to 15 minutes under laminar flow cabinet then stored at 3 to 5°C overnight.The following day, a core of the diet was initially transferred to each well using flamed iron core-cutter (1 cm diameter) under a laminar flow. A core diet was placed each of the 6 wells of the plates. Approximately 40 µl of the treatments were then applied across the surface of diet core (0.34 cm<sup>3</sup>). The following day, a core of the diet was initially transferred to each well using flamed iron core-cutter (1 cm diameter) under a laminar flow. A core diet was placed each of the 6 wells of the plates. Approximately 40 µl of the treatments were then applied across the surface of diet core (0.34 cm<sup>3</sup>). Adult were subsequently transferred from the rearing cage into the wells of the 6-well plates containing the diet and treatments using a tube aspirator. For ease of transfer, the adults were cooled in a fridge for 4 to 7 minutes. Each well plate received 3 to 4 adults. Plates were sealed and incubated at 23-25<sup>0</sup>C, 50–90% r.h, L: D 12:12. Adult mortality were recorded on days 1,3, 5, 7 of experiment. </p> <p>To allow comparisons between experiments, data were standardized to the data of the corresponding negative control, usually sterilized tap water, as follows: standardized data = 100 × (data in negative control - data in treatment)/maximum (data in control or in treatment). The distributions of the data were investigated using histograms and QQ normal and detrended normal probability. Skewness and kurtosis of residuals was also observed for normality of influences of treatments on eggs, neonates, or adults. Equality of variances was assessed using Levene’s test. Multiple comparisons were performed using the Tukey HSD post hoc test for data with equal variances and the Games-Howell post hoc test for data with unequal variances. For each tested insecticide, linear and logarithmic regression models were fit to the dose-response data. In case of significant linear or logartimic relathionships, doses leading to 50% or 80% of relative effects (ED <sub>50,80</sub>) were calculated. </p> <p>The raw data as well as the standardised data are available as a csv file on zenodo. </p> <p> </p> <p> </p>
Remote Rapid Visual Screening (RRVS) Buildings Survey Data - DESTRESS - France
<p>The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.<br> Surveyor: Konstantinos G. Megalooikonomou (GFZ-Potsdam)<br> The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). <br> The following attributes are defined (not all are observable in the RRVS survey):<br />code,description<br> lon, longitude in fraction of degrees<br> lat, latitude in fraction of degrees<br> object_id, unique id of the building surveyed <br> MAT_TYPE,Material Type<br> MAT_TECH,Material Technology<br> MAT_PROP,Material Property<br> LLRS,Type of Lateral Load-Resisting System<br> LLRS_DUCT,System Ductility<br> HEIGHT,Height<br> YR_BUILT,Date of Construction or Retrofit<br> OCCUPY,Building Occupancy Class - General<br> OCCUPY_DT,Building Occupancy Class - Detail<br> POSITION,Building Position within a Block<br> PLAN_SHAPE,Shape of the Building Plan<br> STR_IRREG,Regular or Irregular<br> STR_IRREG_DT,Plan Irregularity or Vertical Irregularity<br> STR_IRREG_TYPE,Type of Irregularity<br> NONSTRCEXW,Exterior walls<br> ROOF_SHAPE,Roof Shape<br> ROOFCOVMAT,Roof Covering<br> ROOFSYSMAT,Roof System Material<br> ROOFSYSTYP,Roof System Type<br> ROOF_CONN,Roof Connections<br> FLOOR_MAT,Floor Material<br> FLOOR_TYPE,Floor System Type<br> FLOOR_CONN,Floor Connections</p>
Path Following in Non-Visual Conditions - Screen shots and audio sample
<p>Screen shots and audio file in addition to the publication "Path Following in Non-Visual Conditions" by Alan Del Piccolo, Davide Rocchesso, and Stefano Papetti. Under revision for IEEE Transaction on Haptics (1 June 2018).</p> <p>Developed in Max (https://cycling74.com).</p> <p>Short description:</p> <ul> <li><em>interface.png</em>: the interface for managing the experiment. Output levels, trial repetitions and feedback conditions can be adjusted from here. The underlying Max patch is depicted in "main patch.png"</li> <li><em>main patch.png</em>: the main patch controlling the experiment. It receives the data from the Soundplane's controller (top left), invokes finger position detection and feedback generation (bottom left), manages the trial repetition and feedback modes (bottom center), and enables the adjustment of the feedback levels (right). </li> <li><em>mapToImage.png</em>: the patch that maps the participant's finger position on the Soundplane to the relative position over the loaded path shape. The position is shown by the white circle on the bottom left of the image.</li> <li><em>rolling_feedback.png</em>: the SDT "rolling model" configured for the use in the experiment. Note that the input levels are generated in the track_detection_SP2 patch.</li> <li><em>sdt_rolling.png</em>: a configuration of the SDT "rolling model" adjusted to output a signal similar to the one used in the experiment (see record.wav) as a stand-alone, namely without using the experiment's patches.</li> <li><em>track_detection_SP2.png</em>: the patch that manages the feedback generation (top left), the recording of execution time (bottom left), and the recording of position and force (center).</li> <li><em>record.wav</em>: a recording of the signal used in the experiment for both audio and vibrotactile feedback.</li> </ul>
S12 | NORMANEWS | NormaNEWS for Retrospective Screening of New Emerging Contaminants
<p>This is the collection associated with list S12 NormaNEWS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S12</p> <p>NORMANEWS</p> <p><strong>NormaNEWS for Retrospective Screening of New Emerging Contaminants</strong></p> <p>NormaNEWS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/NormaNEWS_V4_26042017_wDTXSIDs.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/NormaNEWS_V4_26042017_wDTXSIDs.xlsx">XLSX</a> (3/10/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/normanews">NORMANEWS List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/NormaNEWS_V4_InChIKeys.txt">NormaNEWS InChIKeys</a> (8/05/2017)</p> <p><a href="http://www.norman-network.com/?q=node/244">NormaNEWS</a> list provided by Nikiforos Alygizakis, Saer Samanipour and Kevin Thomas.</p> <p>Alygizakis et al 2018, DOI: 10.1021/acs.est.8b00365</p>
SEAL Substudy dataset on psychosocial consequences of liver screening
<p><strong>Background:</strong></p> <p>This data set comprises responses from n=487 patients who took part in a screening program for liver cirrhosis and fibrosis from January 2018 to February 2021 which was implemented as SEAL liver prevention program in Rhineland-Palatinate and Saarland, Germany. The project was funded by the Innovation Fund of Federal Joint Committee of Germany, provided by Deutsches Zentrum für Luft- und Raumfahrt (Funding ID: 01NVF16026).</p> <p>The SEAL program is a prospective study that aimed at evaluating a newly introduced medical screening method for early diagnosis of liver cirrhosis or fibrosis. Within this screening, patients who visited collaborating clinics or doctor's offices for a general health check-up (Check-Up 35), underwent a multistep screening (step 1: blood sample test and determination of risk score, step 2: enhanced laboratory diagnostics and ultrasound, step 3: liver biopsy and enhanced diagnostics in a specialized clinic). Inclusion criteria for study participation were a minimum age of 35 years and no known previous cirrhosis of liver.</p> <p><strong>Data collection:</strong></p> <p>In August 2019, we contacted all participants who had been included in the SEAL program so far. A total of n=5,935 patients were contacted via postal mailing which included a self-administered paper questionnaire, a patient information and an informed consent form. In sum, n=487 processsable questionnaires were returned. The data set contains processed data, so that used instruments were transformed according to guidelines, where available. The data set is completely anonymized.</p> <p><strong>Content:</strong></p> <p>The data set encompasses a context-adapted German version of the Psychological Consequences of Screening Questionnaire (PCQ) (Cockburn et al. 1992, Fichtner et al., 2022), the short form of the State-Trait Anxiety Inventory (STAI) (Marteau et al. 1992), a multimorbidity score (KOMO) (Glattacker et al. 2007), a health literacy instrument (HELP) (Farin et al. 2013), the Oslo Social Support Scale (OSSS-3) (Kocalevent et al. 2018), items to measure communication competences (KoKo) (Farin et al. 2014), the MacArthur Scale on Subjective Social Status (Adler et al. 2000) and items on satisfaction with healthcare (ZAP) (Bitzer et al. 1999). Furthermore, background information was collected: Household size, education, occupational status, age, sex, existence of a steady partnership, professional education, future screening attitudes, reception of test result and satisfaction with screening procedure and information on risk factors.</p>
Spectral transmittance of solar radiation by screens and nets used in horticulture and agriculture
<p>We present a dataset of measurement of the spectral transmittance of 197 horticultural nets and screens from five companies. These materials span a range of uses from shading and reducing the heat load on plants to blocking pests such as birds and insects. Routinely, these materials are used in greenhouses and polytunnels to reduce the sunlight received by plants, however their spectral transmittance is not routinely measured. The spectral irradiance that plants receive can affect plant growth and photomorphogenesis, hence this information is of value when selecting the most appropriate material for a given purpose. The spectral transmittance of the materials was measured outdoors close to solar noon using an array spectrometer calibrated for the range 290-900 nm and compared directly with the ambient solar spectral irradiance. The measured spectrum encompasses those regions perceived by plants through known photoreceptors and used by plants in photosynthesis: ultraviolet (UV); photosynthetically active radiation (PAR), and near infra red (far red – FR).</p> <p>The solar spectral photon irradiance (μmol m<sup>-2</sup> s<sup>-1</sup>) transmitted by screens and nets from several manufacturers was measured with an array spectroradiometer. Our measurements and analyses are focused on the differences in spectral irradiance, created when employing these screens and nets, in order to address the lack of detailed studies of these light environments, rather than the physiochemical properties of materials or their cost-effectiveness. The measurements of spectral irradiance under climate screens, and shade and insect nets, were made on clear days in sunny conditions close to solar noon (between 10 a.m. to 2 p.m local time) at NC State University campus (35.78°N, -78.67°W) in late July and early August 2017, and in Viikki Field Plots at the University of Helsinki (60.22°N, 25.01°E, 55 m asl) in July and August 2018. The methods for measurements at North Carolina State University follow the protocol described below and published in <a href="https://doi.org/10.1371/journal.pone.0199628">Kotilainen et al., (2018)</a>, where a comprehensive assessment of the results of this subset of screens/nets and their meaning is also given.</p> <p>The measurements were performed in an open field with no surrounding structures or buildings within 20 m. Repeated measurements of each different sample were made in a randomised order, thus ensuring comparability among measurements. Measurements were made on a tripod 0.7 m above the ground and the sample was secured to a wooden plate 3 cm above the diffusor. A test, comparing four larger (1 x 1 m) samples against those of the standard dimensions that we used, found that the area of screen/net measured did not affect the results at this distance between the screen/net and diffusor. Thus, there was no evidence that unfiltered diffuse or scattered radiation interfered with measurements despite the relatively small dimensions of the sample.</p> <p>Measurements under each screen/net sample in 2017 (Svensson 13 x 19 cm, Mallas Textiles 8 x 10 cm) were made twice to account for any possible effect of sample placement over the cosine diffuser and change in the sun angle during a set of measurements. Given that no significant differences were evidence, the 2018 screen/net samples (Criado y Lopez 8 x 12 cm, Howitec 15 x 25 cm, Huachang yarns 25 x 30 cm, and Jiangsu Huachang Yarns and Fabrics 8 x 12 cm) were only measurement once. A recording of spectral irradiance without the screen/net of filtered sunlight was made directly before and after each filter measurement (called “Open”).</p> <p>The spectrometer used had been calibrated for measurements of UV and visible solar radiation (Maya2000 Pro Ocean Optics, Dunedin, FL, USA; D7-H-SMA cosine diffuser, Bentham Instruments Ltd, Reading, UK - see <a href="https://doi.org/10.1002/ece3.4496">Hartikainen et al., 2018</a> for details of the measurement protocol). Briefly, each measurement of irradiance transmitted beneath a screen or net was followed by sequence of measurements in the dark and with a polycarbonate filter attenuating all UV radiation. These controls accounted for the dark noise and stray light in the UV waveband. Both a correction for the shape of the slit function and for stray light were included in the post-processing of the spectra (<a href="http://uv4plants.org/methods/how-to-check-an-array-spectrometer/">Aphalo et al., 2016</a>). Bracketing was performed by taking a measurement of the UV region and splicing this together this the entire spectrum. All measurements were processed using the Photobiology packages in R.</p> <p>Measurements of solar spectral irradiance in the wavelength range from 290 nm to 900 nm were processed in R, using the <em>photobiology</em> packages developed for spectral analysis (<a href="https://doi.org/10.19232/uv4pb.2015.1.14">Aphalo, 2015</a>). We present spectral photon irradiance (μmol m<sup>-2</sup> s<sup>-1</sup>) and spectral energy irradiance (W m<sup>-2</sup>). Plants absorbs photons producing a chemical change (Grotthus Law) thus photon irradiance is more easily applicable understanding to biological processes in plants. The spectral transmittance of the screens/nets are the most useful data presented. Essentially the patterns of spectral attenuation will be consistent, irrespective of whether spectra are expressed as photon or energy irradiance.</p> <p>Utilizing predefined functions available in the <em>photobiology</em> packages, we calculated the integrals and photon ratios of these integrals as follows: UVB:PAR 280–315 nm/400-700 nm, UVA:PAR 315–400 nm/400-700 nm, blue:green (B:G) 420–490 nm/500-570 nm, blue:red (B:R) 420–490 nm/620-680 nm. Red and far-red for the calculation of R:FR ratio are 655–665 nm and 725–735 nm, respectively. UVB radiation and UVA radiation are defined according to ISO, blue, green and red according to <a href="https://doi.org/10.1104/pp.110.160820">Sellaro et al. (2010)</a>, and R:FR according to <a href="https://doi.org/10.1146/annurev.pp.33.060182.002405">Smith(1982)</a>.</p> <p>The same definitions of the UV-waveband are maintained for both spectral integrals and their ratios throughout, i.e. according to ISO, (<a href="http://doi:%2010.21273/HORTTECH03648-16">Both et al., 2017</a>). This is because the UVB and UVA wavebands of solar radiation follow distinct daily patterns of variation; UVB irradiance is highest during the four hours around solar noon, whereas the UVA region of solar radiation remains a similar proportion of total irradiance throughout the day. These differences also imply that UVA and UVB radiation follow different diurnal and seasonal patterns of variation (<a href="https://doi.org/10.1111/j.1751-1097.2007.00216.x">Seckmeyer et al., 2007</a>).</p> <p><strong>Data Files Available</strong></p> <p><strong>DataBaseScreensNets.zip</strong></p> <p>Graphs (.jpg files) of actual measured (1) spectral energy irradiance, (2) spectral photon irradiance, and (3) proportion transmittance of solar radiation, for each screen and net. (1) Energy Irradiance figures (suffix _EI.) and (2) Photon Irradiance figures (suffix _PI.) are plot of the measured values of irradiance under the filter (screen/net) and corresponding measurements without the screen or net (“open” measurement) for comparison (290-898 nm wavelength range). The proportion transmittance under each screen or net is calculated from comparison of the open and measured spectrum (suffix _Trans). The low-wavelength tail end of the spectrum is trimmed (<310 nm) in each plots since % transmittance are inflated by low signal to noise ratio in the UV-B region where irradiance values are very low.</p> <p>The database screens and net are identified by the name of the company “_” name of the screen/net for all 197 materials.</p> <p>These figures can be reproduced from the file “ScreensNets_irrad_trans.txt” using the R code “Plotting_DataBaseScreensNets.r”</p> <p><strong>ImagesScreensNets.zip</strong></p> <p>Image files (.jpg files) from photos and scans of each of the measured screens and nets. One image from each of the 197 filter materials (screens/nets) measured is stored in folders arranged according to the company for each filter type. The companies are: Criado y Lopez; HowiTech; Huanchang yarns; Jiangsu Huachang Yarns & Fabrics; Mallas_Textiles and Svensson.</p> <p><strong>ScreensNets_irrad_trans.txt</strong></p> <p>This is the main database file containing the measurements of spectral irradiance beneath each filter material (screen/net) from 290 nm – 898 nm and corresponding open reading, and calculated spectral transmittance.</p> <p>Data are in columns as follows: (A) Company – the Company name; (B) FilterName – the filter name as given by the company; (C) Serial - a serial number, effectively equivalent to the order in which the materials were measured; (D) wavelength – at intervals recorded by the array spectrometer running for each spectrum from 290.02 nm to 897.73 nm; (D) FilterEI - energy irradiance of transmitted solar radiation measured 3 cm beneath the filter material (screen/net) at each wavelength of the spectrum; (E) FilterPI – photon irradiance equivalent to the energy irradiance; (F) OpenEI – energy irradiance of solar radiation at the same location without the filter material (screen/net) (G) OpenPI – photon irradiance equivalent to the energy irradiance; (H) FilterFactor – the proportion of radiation transmitted by the filter material (screen/net) at each wavelength measured, a value between 0.0 and 1.0 (values out of range at low wavelengths in the UV-B region are replaced with 0.0 or 0.1).</p> <p>Processed spectra are given: processing of raw spectra was done with <em>Photobiology</em> packages in R. Full spectra were recorded with an integration time set manually to give maximum counts of just less than 60 000 at the wavelength corresponding to peak spectral irradiance. Bracketing was performed by recording a second spectrum (long spectrum) with ten-times longer integration time than this, to achieve greater accuracy of measurement in the UV region (< 400 nm). These two spectra were spliced together. Each filter measurement was accompanied by a dark measurement (to estimate dark noise) and a measurement under a polycarbonate filter (PC) to correct for stray light. In 2018, these two readings were performed immediately after the filter material (screen/net) was measured; both within 10 s total of the filter material measurement for both the full spectrum, and long spectrum.</p> <p><strong>ScreensNets_irrad_trans.xlsx</strong></p> <p>This Excel file contains the same information in columns as the file ScreensNets_irrad_trans.txt but with a second worksheet showing the trimming calculations for out-of-range readings at low UV-B wavelength and with an addition final column, the irradiance spectrum open29_irrad (described below).</p> <p><strong>Open29_irrad.txt</strong></p> <p>In order to obtain standardised BSWF files to comparison with each other, the calculated proportion spectral transmittance results for each filter material (screen/net) were applied to a “standard” solar-noon open-spectrum from Helsinki recorded on a date close to midsummer (Open29_irrad.txt). This spectrum was measured as described above.</p> <p>This spectrum was measured at Viikki Fields, Helsinki on Wed June 27<sup>th</sup> 2018 at 13:15:33 EEST (Integration Time, 110000 μsec; bracketting x10) in a completely open area.</p> <p>To apply the transmittance data to their own locations, database users should substitute the spectrum from their own location for Open29_irrad.txt to obtain spectral irradiance data for the effects of the filter materials (screens/net) at their site using the R code Calculating_Spectral_Integrals.r</p> <p><strong>ScreensNets_spectral_integrals.txt</strong></p> <p>The file gives a matrix of spectral integrals and ratios calculated with the <em>Photobiology</em> packages in R for each of the spectra presented in ScreensNets_irrad_trans.txt. Column headings are the filter material ID, made up from the “Company name” “_” “filter name”. The first column contains row names identifying spectral integrals and ratios calculated – first as energy irradiance then as photon irradiance and finally as photon ratios. Calculations are made using the BSWF (<strong>Spectral_Integrals_Function.r</strong>) as follows: PAR_e; UVB_e; UVA_e; UVb350_e; UVa350_e; Blue_e; Green_e; Red_e; Far_red_e; GEN_G_e; GEN_T_e; PG_e; DNA_N_e; CIE_e; FLAV_e; Infra_red_e; PAR_q; UVB_q; UVA_q; UVb350_q; UVa350_q; Blue_q; Green_q; Red_q; Far_red_q; GEN_G_q; GEN_T_q; PG_q; DNA_N_q; CIE_q; FLAV_q; Infra_red_q; UVB_UVA; UVB_PAR; UVA_PAR; R_FR_Sellaro; R_FR_Smith10; R_FR_Smith20; B_G; B_R; PhyEqi.</p> <p><strong>ScreensNets_spectral_integrals.xlsx</strong></p> <p>This files contains the same data as ScreensNets_spectral_integrals.txt and shows on individual worksheets, processing of original, smoothed (in Photobiology package to improve the signal to noise in the UV-B tail of the spectr), and corrected (with values of transmittance greater than 1.0 or less than 0.0 replaced in the UV-B tail) data; and comparisons of the Original vs. Corrected, and Original vs. Smoothed data. The same BSWF calculations for the example open spectrum open29_irrad (used for standardisation) are given on their own worksheet, as is the corresponding “FilterFactor” (proportion spectral transmittance) for each spectral integral and spectral photon ratio. The final worksheet “Type” lists the filters and their expected function (i.e. shade, pest net, hale net, ground cover etc.).</p> <p>This “FilterFactor” information could be of practical use in situations where the spectral irradiance is unavailable for a given location, and comparisons among filters need to be made from only partial data (e.g. PAR PPDF). These FilterFactors can be applied to the PAR PPDF for instance to calculate the daily light integral through the day for horticultural proposes. Please note that differences in the shape of the solar spectrum at different locations will cause (small) deviations in the transmitted PAR PPFD calculated from the spectral integral compared with the more precise calculation from the spectral irradiance. Although for the purposes of comparison between filters these are likely to be of minor importance. </p> <p><strong>Plotting_DataBaseScreensNets.r</strong></p> <p>This file gives the R code for plotting the graphs in DataBaseScreensNets.zip from the source file ScreensNets_irrad_trans.txt. Make sure that the required packages are loaded. The code was run in R version 3.4.3.</p> <p><strong>Calculating_Spectral_Integrals.r</strong></p> <p>The file gives the R code to calculate spectral integrals and to include an open measurement for standardisation (Open29_irrad) from the source file ScreensNets_irrad_trans.txt (as described above). The spectra in ScreensNets_irrad_trans.txt are converted to source.spct for use in the Photobiology packages.</p> <p><strong>Spectral_Integrals_Function.r</strong></p> <p>The file is a function requiring the Photobiology packages in R to run. It is needed to calculate the spectral integrals described above and can be amended to obtain whichever spectral integrals and photon ratios from the Photobiology packages are desired.</p>
Dataset for "Low-temperature processing of screen-printed piezoelectric KNbO3 with integration onto biodegradable paper substrates"
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Low-temperature processing of screen-printed piezoelectric KNbO3 with integration onto biodegradable paper substrates”.</p> <p><strong>Associated Manuscript Abstract:</strong> "The development of fully solution-processed, biodegradable piezoelectrics is a critical step in the development of green electronics towards the worldwide reduction of harmful electronic waste. However, recent printing processes for piezoelectrics are hindered by the high sintering temperatures required for conventional perovskite fabrication techniques. Thus, a process was developed to manufacture lead-free printed piezoelectric devices at low temperatures to enable integration with eco-friendly substrates and electrodes. A printable ink was developed for screen printing potassium niobate (KNbO<sub>3</sub>) piezoelectric layers in microns of thickness at a maximum processing temperature of 120 °C with high reproducibility. Characteristic parallel plate capacitor and cantilever devices were designed and manufactured to assess the quality of this ink and evaluate its physical, dielectric, and piezoelectric characteristics; including a comparison of behavior between conventional silicon and biodegradable paper substrates. The printed layers were 10.7–11.2 μm thick, with acceptable surface roughness values in the range of 0.4–1.1 μm. The relative permittivity of the piezoelectric layer was 29.3. The poling parameters were optimized for the piezoelectric response, with an average longitudinal piezoelectric coefficient for samples printed on paper substrates measured as <em>d</em><sub>33,<em>eff</em>,<em>paper</em></sub> = 13.57 ± 2.84 pC/N; the largest measured value was 18.37 pC/N on paper substrates. This approach to printable biodegradable piezoelectrics opens the way forward for fully solution-processed green piezoelectric devices."</p> <p>The data set consists of the following folders:</p> <ul> <li>Device design files <ul> <li>Contains data associated with the design and fabrication of printed devices.</li> <li>Includes CAD designs of fabricated devices and py files of computational models</li> </ul> </li> <li>Physical characterization data <ul> <li>Contains data associated with characterizing the physical properties of the piezoelectric devices</li> <li>Includes particle size analysis data (SEM images of printed layers, collected dimensional data), Profilometry scans, Layer adhesion, ink density, and rheology data.</li> </ul> </li> <li>Dielectric characterization data <ul> <li>Contains data associated with characterizing the dielectric properties of the piezoelectric devices </li> <li>Includes py analysis script as well as raw data collected for capacitor devices of varying surface area and substrate material</li> </ul> </li> <li>Piezoelectric characterization data <ul> <li>Contains data associated with characterizing the piezoelectric properties of the devices</li> <li>Includes raw data collected from Berlincourt measurements of samples, matlab scripts for analysis of cantilever samples from Laser Doppler Vibrometry, and raw data collected for cantilever samples of device deflection from LDV measurements</li> </ul> </li> </ul> <p>Please refer to the included readme files for a detailed description of the contents.</p>
High-Throughput Density Functional Theory Screening of Double Transition Metal MXene Precursors
<p>This dataset contains density functional theory results on a set of double-transition metal MXene precursors</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.