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8,038 results for “validation”
Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation
<p>Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia.</p> <p>IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of:</p> <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> <p>Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as:</p> <p><code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code></p>
Fig. 2. A in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 2. A haplotype network based on cytochrome oxidase I (CO1) fragment using 112 previously published GenBank sequences and representing 20 genetic clades numbered as in Palandačić et al. (2017, 2020), of which 12 are considered valid species including P. chrysoprasius (Pallas, 1814) and Kuban' Phoxinus (an available species name not known). The lines carry the number of mutational steps (shown in red).
Fig. 1 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 1. Map of the localities of Phoxinus Agassiz, 1835 in river drainages of the northern and north-eastern coasts of the Black Sea and the Caspian Sea based on numerous published sources (references available in Supp. file 1) and public museum collections (Museum of Zoology, National Museum of Natural History, Kyiv, Ukraine; Natural History Museum, Vienna, Austria; Zoological Research Museum Alexander Koenig, Bonn, Germany). Coloured circles and numbers correspond to genetically examined individuals of clades specified in Palandačić et al. (2017, 2020): yellow circle = P. marsilii, Clade 9; black circles = 'Baltic Phoxinus', Clade 17; grey circles = P. colchicus, Clade 18; purple circle = 'Kuban Phoxinus', Clade 19; pink circle = 'Crimean Phoxinus', Clade 20.
Fig. 4 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 4. DFA based on 11 counts, two coded characters, and 55 relative measurements, for males and females separately (A) and for females only (B). Numbers of samples or clades as in Fig. 3. Abbreviations: f = females; m = males. DFA statistics values: A. Wilks' Lambda 0.00000, approx. F (156.189) = 11.390, p <0.0000. B. Wilks' Lambda 0.00002, approx. F (48.48) = 36.279, p <0.0000 (perfect discrimination).
Fig. 3 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 3. DFA based on 11 counts and 2 coded characters as in Supp. file 4. Clade 5 = 5a, Danubian tributaries in Bulgaria (Nishava, Beli Vit, and Palakaria samples and P. csikii Hankó, 1922 from type locality). Clade 14 = non-Danubian rivers of the Black Sea coast in Bulgaria (14a, Izvorska, Veleka, Karaagach and Kamchiya) and Turkey (14b, Gönen and Sapanca). Clade 9 = P. marsilii Heckel, 1836. Clade 20 = Crimea, Salhir. DFA statistics values: Wilks' Lambda 0.16694, approx. F (45.1345) = 14.709, p <0.0000 (perfect discrimination).
Fig. 5. A in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 5. A. Neotype of Phoxinus chrysoprasius (Pallas, 1814), ♂ (ZFMK 93640-59). B. ♀ (ZFMK 93640- 59), 87.2 mm SL, same locality and date as the neotype.
Validated names for experimental studies on race and ethnicity
<p>A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers need pre-tested names with data on perceptions of these attributes. Such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.</p> <p>License: CC-By Attribution 4.0 International</p> <p> </p>
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
Describing ion transport and water splitting in an electrodialysis stack with bipolar membranes by a 2-D model: Experimental validation
<p>Electrodialysis with bipolar membranes (EDBM) has drawn attention motivated by their application in gener- ating reagents from salts. Due to the water splitting (WS) occurring at the junction of the bipolar membranes (BPMs), where the anion and cation layers are in strict contact, H+ and OH- are released from the BPM producing acid and alkali on the respective compartment. Considering this application, the interest of this work is to provide further understanding of the mechanisms of WS and transport of species in EDBM. This work develops and utilizes, for the first time, an experimentally validated two-dimensional (2-D) computational model, in which the Navier-Stokes and Nernst-Planck equations are coupled with the description of WS given by the Second Wien effect. In addition, a 1-D geometry is also proposed to perform a comparison between electroneutrality and Poisson charge conservation. The model is computationally solved using COMSOL Multiphysics. According to simulations, electroneutrality is valid for 2-D geometries. Moreover, the semipermeable characteristics of the membranes are assessed by means of evidencing a polarization effect resulting in a double-electric layer. The model proposed predicts a significant proton leakage, and facilitates the study of WS within the BPMs.</p>
Datasets for validating Harmony
<p>Harmony is a data harmonisation project that uses Natural Language Processing to help researchers make better use of existing data from different studies by supporting them with the harmonisation of various measures and items used in different studies. Harmony is a collaboration project between the University of Ulster, University College London, the Universidade Federal de Santa Maria in Brazil, and Fast Data Science Ltd.</p> <p>You can read more at <a href="https://harmonydata.org/">https://harmonydata.org</a>.</p> <p>There is a live demo at: <a href="https://app.harmonydata.org/">https://app.harmonydata.org/</a></p> <p>These are the datasets used to validate Harmony. The Excel file is McElroy et al's data, and the zip file contains the English and Portuguese GAD-7s.</p>
DeliCS Training+Validation Data - SPI-TGAS-MRF+GRE
<p>This data set consists of raw MRI k-space data from 12 healthy volunteers. The data were acquired on a 3T Premier MRI scanner (GE Healthcare, Waukesha, WI) with a 48-channel head receiver-coil. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. </p> <p>SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>):</p> <p>The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats)</p> <p>GRE (files named <strong>raw_gre.npy</strong>):</p> <p>A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64))</p> <p>Noise estimation (files named <strong>noise.npy</strong>):</p> <p>Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points)</p> <p>To run the processing pipeline presented in [1], please follow the instructions on <a href="http://github.com/SetsompopLab/deli-cs">https://github.com/SetsompopLab/deli-cs</a> and download Zenodo datasets <a href="https://doi.org/10.5281/zenodo.7734431">10.5281/zenodo.7734431</a> and <a href="http://doi.org/10.5281/zenodo.7703200">10.5281/zenodo.7703200</a> with testing data and meta data needed for the reconstruction pipeline.</p> <p> </p> <p>[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em><a href="https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1">https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1</a></p> <p>[2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:<a href="https://doi.org/10.1002/mrm.29194">10.1002/mrm.29194</a></p>
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
<div> <div> <div> <div> <div>This dataset includes experimental measurements taken on a laboratory testbed at Colorado State University that was used for model validation of a software toolkit, the Building Electrical Efficiency Analysis Model (BEEAM). This toolkit was developed for comparing electrical efficiency of AC versus DC distribution systems in buildings. The testbed emulated loads found in a small office building and included laptop computer chargers, LED lighting systems, and miscellaneous DC and AC loads. Measurements were taken under AC and DC configurations in electrically balanced and unbalanced loading conditions. Also included in the dataset is an uncertainty analysis. A complete description of the testbed, hardware, measurements and uncertainty analysis is contained in the paper cited below.</div> </div> </div> </div> </div> <div> </div> <div>Avpreet Othee, James Cale, Arthur Santos, Stephen Frank, Daniel Zimmerle, Omkar Ghatpande, Gerald Duggan and Daniel Gerber, <em>"A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings," Energies, 2023 (accepted, publication in progress).</em> </div>
Data sample INDIP Validation Paper
<p>Standardized data from Mobilise-D HYA dataset (one participant, data from both laboratory and 2.5 hours) and Mobilise-D technical validation dataset (one subject for each cohort - HOA, PD, MS, COPD, CHF, PFF; data from 2.5 hours) are provided in the shared folder, as an example of the the work proposed in the publication "A multi-sensor wearable system for the assessment of diseased gait in real-world conditions" that has been accepted for publication in Frontiers Journal. Please refer to that publication for further information. Please cite that publication if using these data. </p>
VoroIF-GNN training, validation, and testing data
<p>Data used to train, validate, and test the VoroIF-GNN method described in the paper "VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network".</p>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</p>
Unlabeled Sentinel 2 time series dataset (validation): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only validation data</strong> are available. To download the full pretraining dataset, see <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UVU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
LEMON2021 Ground validation campaign
<p><strong>The LEMON CRDS ULA dataset consists of geolocated observations of humidity, water vapor isotopic composition, temperature and atmospheric pressure acquired with an ultralight aircraft (ULA) over the area of Aubenas (France) between 17/09/2021 and 23/09/2021. Data is provided in NetCDF format with 3 different averaging times for each flight (2, 5, 10 seconds). Take off location is an airstrip next to the Lanas Airfield (44.5393° N, 4.3679° E, 281 m ASL). </strong></p>
Data from: Spatial localization of anterior precuneus for bodily self validated with brain stimulation
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>The posteromedial cortex (PMC), comprising the precuneus, the posterior cingulate, and the retrosplenial regions, is known to be engaged in various self-referential functions. Recent observations have also suggested a link between PMC dysfunction and self-dissociation. To test the causal relevance of specific PMC locations for self- referential processing, we recruited nine neurosurgical participants with bilaterally implanted PMC electrodes. We applied focal electrical stimulation within discrete PMC sites while probing changes in the participants' subjective states. We found that, in all nine participants, the electrical perturbation of the anterior precuneus (aPCu), but not the other PMC sites, caused apparent dissociative changes in the physical and spatial bodily domain involving head, trunk, and legs. The responsive aPCu sites did not exhibit event-related activation during a cued autobiographical memory recall task. Furthermore, resting-state functional connectivity with functional Magnetic Resonance Imaging data and effective connectivity with single-pulse electrical stimulation procedures confirmed that the responsive aPCu sites were distinct from, while closely connected with, the adjacent PMC nodes of the default mode network (DMN). Based on these data, we conclude that the aPCu is a distinct functional unit within the PMC and causally important for processing self-referential information in the physical and spatial domains. Future larger-scale experimental studies are needed to explore how operations of distinct neuronal populations within the PMC are integral to various cognitive processes that require a reference to self in its various dimension.</p> </div> </div> </div> </div>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Polygenic risk scores validated in patient-derived cells stratify for mitochondrial subtypes of Parkinson's disease
<p><strong>Background</strong> Parkinson’s disease (PD) is the fastest growing neurodegenerative disorder, with affected individuals expected to double during the next 20 years. This raises the urgent need to better understand the genetic architecture and downstream cellular alterations underlying PD pathogenesis, in order to identify more focused therapeutic targets. While only ∼10% of PD cases can be clearly attributed to monogenic causes, there is mounting evidence that additional genetic factors could play a role in idiopathic PD (iPD). In particular, common variants with low to moderate effect size in multiple genes regulating key neuroprotective activities may act as risk factors for PD. In light of the well-established involvement of mitochondrial dysfunction in PD, we hypothesized that a fraction of iPD cases may harbour a pathogenic combination of common variants in nuclear-encoded mitochondrial genes, ultimately resulting in neurodegeneration.</p> <p><strong>Methods</strong> to capture this mitochondria-related “missing heritability”, we leveraged on existing data from previous genome-wide association studies (GWAS) – i.e., the large PD GWAS from Nalls and colleagues. We then used computational approaches based on mitochondria-specific polygenic risk scores (mitoPRSs) for imputing the genotype data obtained from different iPD case-control datasets worldwide, including the Luxembourg Parkinson’s Study (412 iPD patients and 576 healthy controls) and the COURAGE-PD cohorts (7270 iPD cases and 6819 healthy controls).</p> <p><strong>Results</strong> applying this approach to gene sets controlling mitochondrial pathways potentially relevant for neurodegeneration in PD, we demonstrated that common variants in genes regulating <em>Oxidative Phosphorylation (OXPHOS</em>-PRS<em>)</em> were significantly associated with a higher PD risk both in the Luxembourg Parkinson’s Study (odds ratio, OR=1.31[1.14-1.50], <em>p</em>=5.4e-04) and in COURAGE-PD (OR=1.23[1.18-1.27], <em>p</em>=1.5e-29). Functional analyses in primary skin fibroblasts and in the corresponding induced pluripotent stem cells-derived neuronal progenitor cells from Luxembourg Parkinson’s Study iPD patients stratified according to the <em>OXPHOS</em>-PRS, revealed significant differences in mitochondrial respiration between high and low risk groups (<em>p</em> < 0.05). Finally, we also demonstrated that iPD patients with high <em>OXPHOS</em>-PRS have a significantly earlier age at disease onset compared to low-risk patients.</p> <p><strong>Conclusions</strong> our findings suggest that OXPHOS-PRS may represent a promising strategy to stratify iPD patients into pathogenic subgroups – in which the underlying neurodegeneration is due to a genetically defined mitochondrial burden – potentially eligible for future, more tailored mitochondrially targeted treatments.</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.