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8,038 results for “validation”
A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox
<p>Data set used in "A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox"</p>
Internal and Predictive Validity of the French Health of the Nation Outcome Scales Dataset
<p>This dataset is related to Golay P, Basterrechea L, Conus P, Bonsack C (2016). Internal and Predictive Validity of the French Health of the Nation Outcome Scales: Need for Future Directions. PLoS ONE 11(8): e0160360. doi:10.1371/journal.pone.0160360.</p> <p>http://journals.plos.org/plosone/article/related?id=10.1371%2Fjournal.pone.0160360</p> <p><br /> It includes 19 variables and 2722 cases.</p>
FIGURE 5 in Diaphorodoris alba Portmann & Sandmeier, 1960 is a valid species: molecular and morphological comparison with D. luteocincta (M. Sars, 1870) (Gastropoda: Nudibranchia)
FIGURE 5. The tree portrays the phylogenetic relationships based on the H 3 + COI + 16 S combined dataset. Numbers at nodes are Bayesian posterior probability (pp., left) and ML bootstrap support (bs., right), respectively.
FIGURE 2 in Diaphorodoris alba Portmann & Sandmeier, 1960 is a valid species: molecular and morphological comparison with D. luteocincta (M. Sars, 1870) (Gastropoda: Nudibranchia)
FIGURE 2. SEM images of the buccal apparatus from Diaphorodoris alba (A, C, E) and D. luteocincta (B, D, F) specimens at different magnification levels.
FIGURE 2 in A taxonomic revision of the Musician Wren, Cyphorhinus arada (Aves, Troglodytidae), reveals the existence of six valid species endemic to the Amazon basin
FIGURE 2. Principal components analysis of morphometric characters of taxa of the Cyphorhinus arada complex, showing lack of mensural differentiation among these taxa.
FIGURE 4 in A taxonomic revision of the Musician Wren, Cyphorhinus arada (Aves, Troglodytidae), reveals the existence of six valid species endemic to the Amazon basin
FIGURE 4. Distribution of recognized species within the Cyphorhinus arada complex. Black symbols represents skins and white symbols represents tape recordings. Cyphorhinus arada: triangles; Cyphorhinus transfluvialis: pentagons; Cyphorhinus salvini: squares; Cyphorhinus modulator: circles; Cyphorhinus interpositus: inverted triangles; Cyphorhinus griseolateralis: diamonds. Stars represent the type locality of each species. Illustrations by Laura Montserrat and Michelle Konig.
FIGURE 3 in A taxonomic revision of the Musician Wren, Cyphorhinus arada (Aves, Troglodytidae), reveals the existence of six valid species endemic to the Amazon basin
FIGURE 3. Sonograms of songs typical of each recognized taxon. A and B: Cyphorhinus arada (XC 65012, XC 54717); C and D: Cyphorhinus transfluvialis (ML 113170, ML 113171); E and F: Cyphorhinus modulator (XC 39726, ML 126971); G and H: Cyphorhinus salvini (ML 28625, XC 72499); I and J: Cyphorhinus interpositus (USP 0 792, Kleber 2538); K and L: Cyphorhinus griseolateralis (XC 39960, ML 117074).
Validation data set for automatic blood vessel segmentation in colorectal cancer histology (IHC)
<p><strong>Content</strong></p> <p>This data set contains 100 histological image patches of 1000 * 1000 px size. The samples were immunostained for CD34 (3,3'-Diaminobenzidine, DAB [brown]) with hematoxylin (blue) counterstain.</p> <p>Furthermore, the data set contains a table of blood vessel counts in each image by three blinded observers as well as an automatic count with a method based on the following paper:</p> <p>Kather, Jakob Nikolas et al. "Continuous Representation Of Tumor Microvessel Density And Detection Of Angiogenic Hotspots In Histological Whole-Slide Images". <em>Oncotarget</em> 6.22 (2015): 19163-19176. http://dx.doi.org/10.18632/oncotarget.4383</p> <p><strong>Image format</strong></p> <p>All images are RGB, 0.50 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors and liver metastases) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).</p> <p><strong>Ethics statement</strong></p> <p>All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the Declaration of Helsinki.</p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br> Dr. Jakob Nikolas Kather<br> http://orcid.org/0000-0002-3730-5348<br> ResearcherID: D-4279-2015</p>
Opinions on Internal and External Validity
<p><strong>Overview of Data</strong></p> <p>1) studies.csv : Literature survey of papers from ESEC/FSE, ICSE, and EMSE. Contains data on how they were validated.<br /><br> 2) resultsComplete.csv : Contains the responses of the program-committee members and our categorization of the responses.</p> <p><strong>Attribute Information</strong></p> <p>1) studies.csv:<br /><br> Contains name of the paper, conference and response for the following 5 questions<br /><br> - Was an empirical method applied?<br /><br> - Were the experimental subjects human or non-human?<br /><br> - Were the human experimental subjects professionals or students?<br /><br> - Was an internal or external replication reported?<br /><br> - How are threats to validity described?<br /><br> <br /><br> 2) resultsComplete.csv<br /><br> Contains the responses of the program-committee members and our categorization of the responses<br /></p>
Data from: Area of habitat maps and validated occurrences for neotropical birds of conservation concern
<p>Understanding species distributions is essential for advancing bird conservation, especially in the rapidly changing landscapes of the Neotropics, where habitat loss and degradation are accelerating. Area of Habitat (AOH) maps offer valuable spatial tools for illustrating species distributions by highlighting potentially suitable habitats within their geographic range. In this study, we generated AOH maps for 713 neotropical bird species of conservation concern, which includes species listed as globally or nationally threatened, endemic, or with restricted ranges. Using primary biodiversity data and a structured geospatial workflow, we refined approximately 2.5 million occurrence records through a flagging process and validated 50,743 records manually.<strong> </strong>This unparalleled effort led to the creation of high-quality AOH maps, along with altitude-corrected Extent of Occurrence (EOO-DEM) and Inverse Distance Weighted (IDW) range maps. Our AOH maps significantly improved species distribution predictions for 82% of species, over EOO-DEM maps. The validated occurrences and AOH maps produced in this study have wide-ranging applications, providing a valuable basis for the development of new species distribution models and for evaluating species’ natural history, extinction risk, and habitat threats. They also support the identification of priority areas for strategic conservation investments. Importantly, these maps played a key role in systematic conservation planning analyses for the Conserva Aves initiative, which is facilitating the creation of more than 80 new protected areas across Latin America, safeguarding 2 million hectares and improving the management of an additional 2 million hectares (<a href="https://conserva-aves.org/">https://conserva-aves.org/</a>).</p>
In situ dataset for initialization and validation of the Copernicus Med-MFC biogeochemical model system (MedBGCins)
<p>The biogeochemical model system in use by the Mediterranean Monitoring Forecasting Centre (Med-MFC) of the EU Copernicus Marine Service requires several observational datasets for data assimilation and model initialization and validation (Coppini et al., 2023; Cossarini et al., 2021; Salon et al., 2019). The present MedBGCins dataset consists of the in situ measurements, coming from selected platforms, on which the initialization and validation of the biogeochemical model system are built. The MedBGCins dataset collects in situ measurements along the Mediterranean Sea water column and during the 1995-2023 time period for nutrients (i.e., nitrate, nitrite, phosphate, silicate, ammonium), dissolved oxygen, dissolved inorganic carbon, total alkalinity, total scale pH at 25°C. The dataset also provides pCO2 and total scale pH at in situ conditions, reconstructed by using the PyCO2SYS Python toolbox (Humpreys et al., 2024). The complete list of variables is indicated in Table 1. The largest subset of the original data are from EMODnet Chemistry Mediterranean Sea - Eutrophication and Acidity aggregated datasets 1911/2022 v2023 (reference in Table 2), including both profiles and time series, plus other documented cruises (same table).</p> <p>Additional information and references are included in the UserGuide file.</p> <p> </p>
Mobilise-D Technical Validation Study (TVS) dataset
<h1>Mobilise-D Technical Validation Study (TVS) Dataset</h1> <p>This dataset was recorded as part of the Mobilise-D project, a comprehensive initiative aimed at developing and validating digital solutions for assessing mobility in real-world environments. The Mobilise-D project seeks to address the critical need for accurate, reliable, and scalable tools to monitor and evaluate gait and mobility patterns, particularly in populations with mobility impairments.</p> <p>The dataset comprises recordings from a diverse cohort of participants, including healthy individuals and patients with various mobility-related conditions. Data collection was conducted using state-of-the-art wearable sensors and devices, capturing a wide range of gait parameters and contextual information in both controlled and free-living settings. The primary objective was to ensure the robustness and precision of digital mobility assessment tools under real-world conditions.</p> <p>Key features of the dataset include:</p> <ul> <li> <p>Demographic & Clinical Data: Age, gender, height, weight, and clinical diagnoses.</p> </li> <li> <p>Sensor Data: Raw and processed data from accelerometers, gyroscopes, and other wearable sensors.</p> </li> <li> <p>Reference Gait Parameters: Stride length, stride frequency, gait speed, and variability measures.</p> </li> </ul> <p>The dataset has undergone rigorous validation processes to confirm its accuracy and reliability. It serves as a critical resource for researchers and developers aiming to enhance digital health technologies and improve clinical assessments of mobility. The TVS dataset paves the way for future innovations in digital biomarkers and personalized healthcare solutions.</p> <h2>Brief Overview</h2> <p>This dataset contains data from 108 participants from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). Data was recorded across five measurement sites. Data availability varies between participants, and some tests might be missing for some participants.</p> <p>The recording was split into a comprehensive in-lab assessment and a 2.5 hour unsupervised free living conditions. For the in-lab measurements reference information from marker-based motion capture systems and the multi-device wearable <a href="https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1143248/full">INDIP system</a> are provided. For the free-living recording, only the INDIP system is available as a reference.</p> <p>Participants wore a McRoberts MM+ IMU at the lower back. Some participants additionally wore a custom wrist-IMU at the non-dominant hand. The IMUs were synchronized with the reference system.</p> <p>The following tests were performed as part of the In-Lab data capture (Name in recording file in parentheses):</p> <ol> <li> <p>Timed-Up-and-Go (Test4)</p> </li> <li> <p>Straight Walk Comfortable (Test5)</p> </li> <li> <p>Straight Walk Slow (Test 6)</p> </li> <li> <p>Straight Walk Fast (Test 7)</p> </li> <li> <p>L-Test (Test8)</p> </li> <li> <p>Surface Test (Test9)</p> </li> <li> <p>Hallway Test (Test10)</p> </li> <li> <p>Simulated daily activities (Test11)</p> </li> </ol> <p>For some tests multiple <em>trials</em> are available. Additional trials were performed in the case of technical or performance issues. Hence, the last trial of each test should always be preferred.<br>Tests not listed above (e.g. Test 1-3) are non-walking tests used for calibration. When performing gait related operations, these tests should be excluded.</p> <p>For the free-living recording <em>Recording4</em> corresponds to the actual recording. Recording 1-3 only contains calibration recordings that are usually not required.</p> <p>Learn more about the data collection protocol:</p> <blockquote> <p>C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci, P. Brown, et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study." BMJ Open 11, 12 (2021): e050785 (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>).</p> <p>S. Kirsty, T. Bonci, F. Salis, L. Alcock, E. Buckley, E. Gazit, C. Hansen, et al. "Design and Validation of a Multi-Task, Multi-Context Protocol for Real-World Gait Simulation." Journal of NeuroEngineering and Rehabilitation* 19, 1 (2022):141 (<a href="https://doi.org/10/gt55t6">https://doi.org/10/gt55t6</a>).</p> </blockquote> <h2>Files</h2> <p>/data/: Raw data files sorted by cohort/patientId/measurement_condition.<br>/participant_information.xlsx: Basic demographic and clinical information of all participants and "data quality" overview for all recordings</p> <p>For each recording the following files are provided:</p> <ul> <li> <p>infoForAlgo.mat: Reduced set of relevant demographic information that is required to process the data with the Mobilise-D algorithmic pipeline</p> </li> <li> <p>data.mat: Core data file following the <a href="https://www.nature.com/articles/s41597-023-01930-9">Mobilise-D file format</a>. For each trial the data contains the raw sensor data of the lower-back IMU (SU), the raw data of all reference sensors, and calculated gold standard parameters for all relevant gait parameters based on the reference system. For some participants, data from a wrist worn sensor is included.</p> </li> <li> <p>test_list.json: A json file containing all the available tests and trials including the data.mat file. This information is also available via the data.mat file, but the json file is faster to parse and should help with identifying the correct data files to load.</p> </li> </ul> <h2>Tips and Notes</h2> <h3>Data Quality</h3> <p>Depending on the use case, specific trials should not be used. <code>participant_information.xlsx</code> file contains a sheet named data quality, that indicates for each system used, if the data was recorded properly. "0" indicates that the data is not usable at all, "1" indicates that some issues remain. This usually indicates partial or full data loss in a single test or unreliable reference information. These recordings might be usable for certain types of analysis, but should not be used for proper algorithm validation on the dataset. Only recordings with data quality >=2 for all required systems should be used.</p> <h3>Walking Aid Use</h3> <p>The <code>participant_information.xlsx</code> file contains 3 columns with information about walking aid use. The two columns <code>self_reported_indoors</code> and <code>self_reported_outdoors</code> describe the use of walking aids independent of the study context as reported by the patients of the day of the recording. This information might be different from the actual walking aids used during the assessment. This information can be found <code>use_during_lab_assessment</code> column. This information was recorded by the study conductor. For the free-living tasks patients were allowed to use any assistance they needed. Actual use was not recorded for this assessment.</p> <p>In general, only a small number of participants used walking aids within the study. Therefore, we do not recommend analyzing walking aid users as a different group or including walking aid use as a stratifier.</p> <h3>General Notes</h3> <ul> <li> <p>The participant IDs are "double pseudonymized" and do not correspond to data-ids used within the Mobilise-D project or previously published example data</p> </li> <li> <p>The first digit of the participant IDs identifies the recording center. This information might be helpful to identify systematic domain shifts in the data, as different centers used slightly different measurement setups.</p> </li> <li> <p>In case multiple trials are available for a single test, only use the last one when performing algorithm validation to keep the data between the participants balanced.</p> </li> </ul> <h3>Reference Parameters</h3> <p>Below some notes and general recommendation regarding the reference parameters:</p> <ul> <li> <p>Reference parameters are provided on a MicroWb and ContinousWalkingPeriod level. In most cases, you will likely want to work with the information in "ContinousWalkingPeriod" (if you are using mobgap to load the data, this information is simply called "Wb"). Learn more <a href="https://mobgap.readthedocs.io/en/latest/guides/q_and_a.html#walking-bouts-vs-gait-sequences">here</a></p> </li> <li> <p>For in-lab measurements, the Stereophoto (aka. marker-based Mocap system) should be the preferred reference, as parameters are expected to be more accurate. However, due to limitations of the field-of-view of these systems, some walking trails are not completely covered by the references.</p> </li> <li> <p>Neither reference system includes turning information, as no established reference definition could be identified, that would allow for unbiased comparison of parameters.</p> </li> </ul> <p>Learn more about the methods for extracting reference parameters:</p> <blockquote> <p>T. Bonci, F. Salis, K. Scott, L. Alcock, C. Becker, S. Bertuletti, E. Buckley, et al. “An algorithm for accurate marker-based gait event detection in healthy and pathological populations during complex motor tasks” Frontiers in Bioengineering and Biotechnology, section Biomechanics, 10:868928, 2022 (DOI: 10.3389/fbioe.2022.868928).</p> <p>F. Salis , S. Bertuletti, T. Bonci, M. Caruso, K. Scott, L. Alcock, E. Buckley, et al. “A multi-sensor wearable system for the assessment of diseased gait in real-world conditions”. Frontiers in Bioengineering and Biotechnology, 11, 2023 (<a href="https://doi.org/10.3389/fbioe.2023.1143248">https://doi.org/10.3389/fbioe.2023.1143248</a>).</p> </blockquote> <h2>Usage Recommendation</h2> <p>This dataset is designed to validate algorithms and NOT to derive clinical insights from the patient cohorts.</p> <p>This dataset was used to validate the algorithms of the Mobilise-D computational pipeline for lower trunk IMU data. Details on the publications are reported below.</p> <p>Per-Block Validation:</p> <blockquote> <p>M.E. Micó-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, C. Kirk, A. Soltani, A. Küderle, et al. "Assessing Real-World Gait with Digital Technology? Validation, Insights and Recommendations from the Mobilise-D Consortium." <em>Journal of NeuroEngineering and Rehabilitation</em> 20, no. 1 (June 14, 2023): 78 (<a href="https://doi.org/10/gt55qb">https://doi.org/10/gt55qb</a>).</p> </blockquote> <p>Full Pipeline Validation:</p> <blockquote> <p>K., Cameron, A. Kuederle, M.E. Mico-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, A. Soltani, et al. "Estimating Real-World Walking Speed from a Single Wearable Device: Analytical Pipeline, Results and Lessons Learnt from the Mobilise-D Technical Validation Study." Scientific Reports, 14,1, 1754 2024 (<a href="https://doi.org/10.21203/rs.3.rs-2965670/v1">https://doi.org/10.21203/rs.3.rs-2965670/v1</a>).</p> </blockquote> <p>Implementation of these validation procedures are also available via the open-source library <a href="https://github.com/mobilise-d/mobgap/">mobgap</a>.</p> <p>We recommend the use of this library in all use cases, as it provides high level tools to load and process the dataset. Documentation for this can be found in the following examples:</p> <ol> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_04_tvs_data_no_exc.html">The TVS dataset class</a></p> </li> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_01_loading_example_data.html">Working with data in mobgap</a></p> </li> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_02_working_with_ref_data.html">Working with reference data in mobgap</a></p> </li> </ol> <h2>Suggested Citation</h2> <p>When you are working with the data, we suggest the following citation:</p> <blockquote> <p>Küderle, A. (2024). Mobilise-D Technical Validation Study (TVS) dataset [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.13899385">http://doi.org/10.5281/zenodo.13899385</a></p> </blockquote> <p>Please cite our paper in your publications if our repository helps your research.</p> <blockquote> <p>C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study". BMJ Open 11, 12 2021): e050785. (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>).</p> </blockquote> <h2>License and Legal Information</h2> <p>Mobilise-D Technical Validation Study Dataset © 2024 by Mobilise-D Consortium is licensed under CC BY-NC-ND 4.0</p> <h2>Acknowledgments</h2> <p>We extend our gratitude to all participants who contributed to the Mobilise-D project, enabling the comprehensive collection and analysis of mobility data. This work would not have been possible without the dedication and collaboration of the Mobilise-D Consortium members, including researchers, clinicians, and technical staff.</p> <p>We also acknowledge the funding and support provided by the European Union's Horizon 2020 research and innovation program under grant agreement No 820820. Special thanks to our partner institutions and organizations for their invaluable contributions and continued support.</p> <h2>Disclaimer</h2> <p>The Mobilise-D Technical Validation Study Dataset is provided for research purposes only. The Mobilise-D Consortium makes no warranties, express or implied, regarding the accuracy, completeness, or reliability of the dataset. Users of the dataset assume all responsibility for any conclusions drawn from the data.</p> <p>The dataset must be used in accordance with ethical guidelines and applicable laws and regulations. Any publications or presentations based on this dataset should appropriately cite the source. The Mobilise-D Consortium is not liable for any misuse of the dataset or for any direct, indirect, incidental, or consequential damages arising out of the use of the dataset.</p>
Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.
<p><strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. </p><ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>
Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen'
<p>Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' consisting of (1) the complete data set of all data analyzed for the project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' ('Data_Brain-IT-Validation-Qmci_for-publication.xlsx'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
FIGURE 1 in Ancistrocerus capra (de Saussure, 1857), a Valid Species, Not a Synonym of A. antilope (Panzer, 1798) (Hymenoptera: Vespidae: Eumeninae)
FIGURE 1. Heads of Ancistrocerus species in frontal view. A, C. Ancistrocerus antilope (Panzer, 1798) from Crimea. B, D. Ancistrocerus capra (de Saussure, 1857) from New York State. A, B. Females. C, D. Males. Scale bar = 1 mm.
FIGURE 2 in Ancistrocerus capra (de Saussure, 1857), a Valid Species, Not a Synonym of A. antilope (Panzer, 1798) (Hymenoptera: Vespidae: Eumeninae)
FIGURE 2. Male genitalia of Ancistrocerus species. A, B, E, F. Ancistrocerus antilope (Panzer, 1798) from Crimea. C, D, G, H. Ancistrocerus capra (de Saussure, 1857) from Colorado. A, C. Left paramere and volsella, dorsal view. B, D. Left paramere and volsella, medial view. E, G. Aedeagus, dorsal view. F, H. Aedeagus, lateral view. Scale bar = 0.5 mm. Abbreviations: aa = aedeagus apex; cl = cuspis lobe; d = digitus; vl = ventral lobe.
TREXIO files used for the validation tests in the paper entitled 'TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods'.
<p>The TREXIO files used for the validation tests in the paper entitled TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The detail about the TREXIO library is described in the JCP article [J. Chem. Phys. 158, 174801 (2023)] and the GitHub repository [https://github.com/TREX-CoE/trexio]. The TREXIO files were generated using TREXIO version 2.3.2 (and the corresponding Python API version 1.3.2).</p>
Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level
<p>In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. </p><p>In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:</p><ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>
Validation and Benchmark Dataset for Discrete Element Method Simulations
<p>Verification and Benchmark Dataset for Discrete Element Method Simulations<br>v3 (05/02/2024)<br>Authors: Jose Salomon, Fernando Patino-Ramirez, Catherine O'Sullivan<br>https://doi.org/10.5281/zenodo.10160309<br>Contact: jjs19@ic.ac.uk<br>--------------------------------------------------------------------<br>Description of the repository:</p> <p>This repository contains a collection of datafiles and scripts that can be employed to validate and benchmark new or existing DEM codes. <br>Two validation cases/folders are considered "FCC_packing" and "Rolling_clump". The benchmark dataset is provided in the "Toyoura_sh" folder.<br>All datafiles and scripts are in the corresponding *.zip files. A detailed description of all cases can be found in the related article.</p> <p>In each of these folders, two sub-folders can be found: (1)"Data" and (2)"Scripts". These folders contain:</p> <p>1)"Data": contains the datafiles to perform the validation or benchmark. Two types of data/folders can be found here: "Raw" and "Filtered".<br>The "Raw" folder contains raw data only. The "Filtered" data contains the post-processed data employed to generate the plots found in the related article.<br>Plots in the related article can be reproduced by using the MATLAB files found in the corresponding data folder.</p> <p>2)"Scripts": contains the LAMMPS scripts used to generate the data files contained in "Data".<br>Indications about how to run these scripts can be found in the "README.txt" file in each folder.</p> <p>In order to reproduce the simulations of this repository, LAMMPS must be built including the "GRANULAR" and "RIGID" packages. Please check the README.txt files in each folder for details.</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>
ScienceDex guides
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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.