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8,038 results for “validation”
Dataset for publication: Validation of large-volume batch solar reactors for the treatment of rainwater in field trials in sub-Saharan Africa, Reyneke et al. (2020). DOI: 10.1016/j.scitotenv.2020.137223
<p>Datasets, Supplementary Information and Water Safety Plan (Assessment Form and Risk Matrix) for the publication: "Validation of large-volume batch solar reactors for the treatment of rainwater in field trials in sub-Saharan Africa" which was published in Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2020.137223).</p>
Fig. 2 in On the taxonomic validity of Indian ground spiders: II. Genera Drassyllus Chamberlin, 1922 and Nodocion Chamberlin, 1922 (Araneae: Gnaphosidae)
Fig. 2. Cryptodrassus khajuriai (Tikader & Gajbe, 1976) comb. nov., ♀, holotype of Drassyllus jabalpurensis Gajbe, 2005 (NZC-ZSI-5452/18). A. Habitus, dorsal view. B. Eyes of the same, dorsal view. C. Epigyne, ventral view. D. Same, dorsal view. E. Label from type bottle. Scale bars: A = 1 mm; B = 0.5 mm; C–D = 0.2 mm.
Fig. 1 in On the taxonomic validity of Indian ground spiders: II. Genera Drassyllus Chamberlin, 1922 and Nodocion Chamberlin, 1922 (Araneae: Gnaphosidae)
Fig. 1. Cryptodrassus khajuriai (Tikader & Gajbe, 1976) comb. nov., ♀, holotype of Drassyllus khajuriai Tikader & Gajbe, 1976 (NZC-ZSI-5043/18). A. Habitus, dorsal view. B. Eyes of the same, dorsal view. C. Epigyne, ventral view. D. Same, dorsal view. E. Label from type bottle. Scale bars: A = 1 mm; B = 0.5 mm; C–D = 0.2 mm.
Fig. 4 in On the taxonomic validity of Indian ground spiders: II. Genera Drassyllus Chamberlin, 1922 and Nodocion Chamberlin, 1922 (Araneae: Gnaphosidae)
Fig. 4. Cryptodrassus ratnagiriensis (Tikader & Gajbe, 1976) comb. nov., ♀, holotype of Drassyllus ratnagiriensis Tikader & Gajbe, 1976 (NZC-ZSI-5042/18). A. Habitus, dorsal view. B. Eyes of the same, dorsal view. C. Epigyne, ventral view. D. Same, dorsal view. E. Label from type bottle. Scale bars: A = 2 mm; B = 0.5 mm; C–D = 0.2 mm.
Supplementary Online Material to the paper: Modelling and empirical validation of carbon stock accumulation during the forest transition in France 1850-2015
<p><strong>Supplementary Online Material to the paper:</strong></p> <p><strong>Modelling and empirical validation of carbon stock accumulation during the forest transition in France 1850-2015</strong></p>
Figure 8 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 8. Fridericia bulbosa (Rosa, 1887) sensu stricto. (A) Anterior body segments (dorsal view); (B) lateral view of segments I–V. (C and D) Clitellum in dorsal (C) and ventrolateral views (D); the elliptical contour in (D) shows the midventral granular field behind the male pores; (E) chylus cells in segment XIV; (F) shallow dorsolateral view of segments IV–V, showing the conspicuous epidermal gland cells; (G) midventral close-up of clitellum revealing the I-shaped male slits and the granular field behind them (contour); (H) dorsal view of segments IV–VI, showing the bent tail of a peptonephridium, the spermathecae and the pharyngeal glands. I, coelomocytes. (A) to (D) from permanent whole-mounted specimens, (E) to (I) from live specimens. Anterior to the right, except in (G) and (H) where anterior is to the top.
Figure 1 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 1. Achaeta borbonica sp. nov. (A) Anterior body half in a dorsal view. Note the intestinal loop in IX; (B) lateral view of segments IV–V, showing the right spermatheca; asterisks indicate the oesophageal dorsal ridge; (C) ventral view of segment V, revealing the closely spaced spermathecal pores; (D) sperm funnel in segment X; (E) lateral view of nephridium in segment VIII; (F) coelomic cavity of caudal segments (lateral view), showing the coarse granulation of the chloragogenous cells and the small size of coelomocytes as compared with the flask-shaped glands. (G and H) Lateral views of clitellum, in vivo (G) and after fixation (H). All except (H) from live specimens. In (A), (D) and (E) anterior to the top; in all others, anterior to the right.
Figure 3 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 3. Achaeta giustii sp. nov. (A) Cephalic region (lateral view); (B) dorsolateral view of segments I–VIII, showing the well-developed pharyngeal glands; (C) dorsal view of clitellum. Note the granular cells bordering the middorsal interruption (dg); (D) shallow lateral view of body wall (segment IX), revealing the lozenge pattern of the longitudinal muscle fibres; (E) lateral view of clitellum, showing the reticulate pattern of the gland cells; (F) lateral view of same clitellum in a deeper optical section, documenting its midventral continuity and middorsal gap; (G) ectal portion of the spermatheca; (H) sperm funnel. All from permanent wholemounted specimens, anterior to the left.
Figure 7 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 7. Fridericia meridiana sp. nov. (A) Anterior body segments (dorsal view). The white arrow points to the small spur of the last pharyngeal glands into VII; (B) clitellum (dorsal view); (C) male pores and midventral interruption of clitellum between and before them; (D) lateral view of clitellum and male opening after fixation; (E) coelomic cavity of segments XI–XII, showing the minute sperm funnel; (F) dorsolateral view of segments IV–V, showing first two pairs of pharyngeal glands and the right spermatheca; (G) coelomocytes; (H) peptonephridium. (A) and (D) from permanent whole-mounted specimens; all others from live specimens. In all, anterior to the right.
Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Validation of ESA CCI SM combined v04.7 vs ESA CCI SM combined v05.2 vs ISMN 20191211 global
QA4SM validation of soil moisture data: ESA CCI SM combined v04.7 vs ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/ccfcee99-1d2a-4ee6-9919-8a3d87601d0b/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - Anomalies and no ISMN flags
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/5f6ae4c5-5115-4022-b489-99f4dda1089f/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - Anomalies and ISMN flagged
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/d8b5f409-2bb3-4580-be0b-0c9c2d71c968/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - without Anomalies and ISMN flagged
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/73ed1e31-eaa5-469a-ab9b-4451e4e4d4df/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - without anomalies and without ISMN flags
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/bbf7f693-74ff-4b03-8f5d-792b7b846b40/. Produced on QA4SM (https://qa4sm.eu)
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning
<p><strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong></p> <p> </p> <p><strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong></p> <p><strong> </strong></p> <p><strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong></p> <p><br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong></p> <p><strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong></p>
Dataset for Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
<p>Dataset of paper "Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation".</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted in an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing. The shared data contains the IQ data of both transmit and receive signals used during the measurement campaign.</p> <p>The file "main.m" shows how to process and plot the shared data.</p>
Datasets used in the Lab Validation of the RADON Verification Tool
<p>This repository contains the datasets that have been used to perform the lab validation of the RADON verification tool. In order to replicate the experiments, please unzip the file "validation-datasets.zip" and run the following command:</p> <pre><code class="language-bash">./run_all.rb {path_to_VT} list.json</code></pre> <p> </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.