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11,198 results for “organs”
Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework
<p>Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework</p> <p>DOI: 10.1002/adma.202110027</p> <p>Boix-Constant, Carla; Garcia-Lopez, Victor; Navarro-Moratalla, Efren; Clemente-Leon, Miguel; Zafra, Jose Luis; Casado, Juan; Guinea, Francisco; Manas-Valero, Samuel; Coronado, Eugenio</p> <p> Adv. Mater. 34, 2110027 (2022)</p>
Transformation Rate Maps of Dissolved Organic Carbon in the Contiguous U.S.
<p>We develop two new maps of the dissolved organic carbon (DOC) transformation rate (\(P_r\)) over the contiguous United States. Those maps are derived by combining the USGS riverine DOC observations, soil organic carbon (SOC) data from two sources—HWSD v1.2 and SoilGrids 2.0, and the watershed characteristics from two existing datasets medium-resolution NHDplus and ScienceBase, and state-of-the-art machine learning techniques. </p>
scmcclelland/joint-mediation-study: Data, Analysis, and Figure Scripts for "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application"
<p>This repository contains data, analysis, and figure scripts to create findings from the manuscript "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application" currently under minor revisions.</p> <p>This release includes updated code, primarily improvements to figures, and a new script for a supplementary map figure.</p>
Cloud Feedbacks and Organized Convection in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2024).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2024).</p> <p>Updated 01 April 2025 to include corrected Index of Organization values.</p>
Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols
<p>A database of organ dose coefficients using MC simulations for a clinically representative virtual population of adult and pediatric XCAT patient models over an expanded set of 21 exam protocols in digital tomosynthesis. </p>
Metadata of " Stability of Selected Hydrogen Bonded Semiconductors in Organic Electronic Devices"
<p>Metadata of " Stability of Selected Hydrogen Bonded Semiconductors in Organic Electronic Devices"</p>
Computational Data supporting "Porous covalent organic nanotubes and their assembly in loops and toroids"
<p>Computational research data supporting the article:<br> Kalipada Koner, Shayan Karak, Sharath Kandambeth, Suvendu Karak, Neethu Thomas, Luigi Leanza, Claudio Perego, Luca Pesce, Riccardo Capelli, Monika Moun, Monika Bhakar, Thalasseril G. Ajithkumar, Giovanni M. Pavan, and Rahul Banerjee, "Porous Covalent Organic Nanotubes and Toroids: A Carbon Nanotube Analogue" </p>
Computational results for the publication "Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting"
<p>This upload contains the computationally obtained atomic coordinates for MOF-303 and MOF-333 at different water loadings.</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - METC Subset
<p><strong>Overview:</strong> This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209 </a>- data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Note #1:</strong> we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.</p> <p><strong>Application: </strong>The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).</li> <li>Resolution: 640x480</li> <li>Number of videos: 212</li> <li>Number of annotation files: 212</li> </ul> <p>Movement codes (in JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Note #2: </strong>The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.</p> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset
<p><strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209</a> - data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: 30 FPS</li> <li>Resolution: 320x240 and 640x480</li> <li>Number of videos: 2427</li> <li>Number of annotation files: 4818</li> </ul> <p>Movement codes (both in CSV and JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>7: Turning off the faucet with a paper towel</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Chemical, optical, and oxidizing properties of three kinds of water-soluble organic matter in PM2.5 from biomass and coal combustion in rural areas in Northwest China
<p>this data set is about the molecular carbon content, light absorption, infrared spectra, and oxidation activity in PM2.5</p>
Detection of neurofilament light chain with label-free electrolyte-gated organic field-effect transistors
<p>The uploaded data include</p> <p>i) transfer curves registered at every NF-L concentration;</p> <p>ii) transfer curves for control experiments;</p> <p>iii) raw AFM data acquired after electrode incubation in 0 M, 1 pM, 100 pM and 10 nM [NF-L].</p> <p>Electrical measurements were acquired in 50 mM PBS, pH 7.4 under static conditions.</p> <p>Anti-NF-L antibodies were immobilized on the gate electrode through cys-Protein G. The gate surface has been subsequently passivated through 11-mercaptoundecyl-triethylene glycol SAM.</p>
Performance of GFN1-xTB for periodic optimization of Metal-Organic Frameworks
<p>GFN-xTB optimised structures of CoRE 2014 and CoRE 2019 structures, with and without lattice optimisation.</p>
Figs. 159–170. Noideattella tsiba, new species. 159. Cephalothorax male, lateral view. 160. Same, dorsal view. 161. Infracoxal grooves male, ventral view. 162. Cephalothorax male, lateral view. 163. Embolus, prolateral view. 164. Same, retrolateral. 165. Palp male, ventral view. 166. Palp female tarsal claw area, apical view. 167. Tarsal organ palp female, dorsal view. 168. Tibia 4 female, prolateral view. 169. Pedicel area male, dorsal view. 170 in Noideattella and Tolegnaro, Two New Genera of Goblin Spiders from Madagascar, with Comments on the Gamasomorphoid and Silhouettelloid Oonopids (Araneae, Oonopidae)
Figs. 159–170. Noideattella tsiba, new species. 159. Cephalothorax male, lateral view. 160. Same, dorsal view. 161. Infracoxal grooves male, ventral view. 162. Cephalothorax male, lateral view. 163. Embolus, prolateral view. 164. Same, retrolateral. 165. Palp male, ventral view. 166. Palp female tarsal claw area, apical view. 167. Tarsal organ palp female, dorsal view. 168. Tibia 4 female, prolateral view. 169. Pedicel area male, dorsal view. 170. Abdomen seta male, dorsal view. Scale bars = 50 µm, except 163–164, 166, 167, 170: 10 µm.
Figs. 79–88. Noideattella gamela, new species. 79. Cephalothorax pars thoracica male, lateral view. 80. Labium female. 81. Palp male, apical view. 82. Palp tarsus female, apical view. 83. Claws and tarsal organ leg 1 female. 84. Abdomen setae male, dorsal view. 85. Embolus, retrolateral view. 86. Palp male, prolateral view. 87. Same, retrolateral view. 88 in Noideattella and Tolegnaro, Two New Genera of Goblin Spiders from Madagascar, with Comments on the Gamasomorphoid and Silhouettelloid Oonopids (Araneae, Oonopidae)
Figs. 79–88. Noideattella gamela, new species. 79. Cephalothorax pars thoracica male, lateral view. 80. Labium female. 81. Palp male, apical view. 82. Palp tarsus female, apical view. 83. Claws and tarsal organ leg 1 female. 84. Abdomen setae male, dorsal view. 85. Embolus, retrolateral view. 86. Palp male, prolateral view. 87. Same, retrolateral view. 88. Embolus, prolateral view. Scale bars = 10 µm, except 148, 153–154: 50 µm.
FIGURES 16–30. Pelicinus marmoratus Simon, male. 16. Claws, leg I, distal view. 17. Same, leg II. 18. Same, leg III. 19. Same, leg IV. 20. Tarsal organ, leg I, dorsal view. 21. Same, leg II. 22. Same, leg III. 23. Same, leg IV. 24. Same, palp. 25. Trichobothrial base, metatarsus II, dorsal view. 26. Palp, prolateral view. 27. Same, retrolateral view. 28. Palpal tibia, dorsal view. 29. Embolus, ventral view. 30 in The Goblin Spider Genus Pelicinus (Araneae, Oonopidae), Part 1
FIGURES 16–30. Pelicinus marmoratus Simon, male. 16. Claws, leg I, distal view. 17. Same, leg II. 18. Same, leg III. 19. Same, leg IV. 20. Tarsal organ, leg I, dorsal view. 21. Same, leg II. 22. Same, leg III. 23. Same, leg IV. 24. Same, palp. 25. Trichobothrial base, metatarsus II, dorsal view. 26. Palp, prolateral view. 27. Same, retrolateral view. 28. Palpal tibia, dorsal view. 29. Embolus, ventral view. 30. Same, retrolateral view.
Imaging of Organic Samples with Megaelectron Volt Time-of-Flight Secondary Ion Mass Spectrometry Capillary Microprobe
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) with MeV primary ions offers a fine balance between secondary ion yield for molecules in the mass range from 100 to 1000 Da and beam spot size, both of which are critical for imaging applications of organic samples. Using conically shaped glass capillaries with an exit diameter of a few micrometers, a high energy heavy primary beam can be collimated to less than 10 μm. In this work, imaging capabilities of such a setup are presented for some organic samples (leucine-evaporated mesh, fly wing section, ink deposited on paper). Lateral resolution measurement and molecular distributions of selected mass peaks are shown. The negative influence of the beam halo, an unavoidable characteristic of primary beam collimation with a conical capillary, is also discussed. A new start trigger for TOF measurements based on the detection of secondary electrons released by the primary ion is presented. This method is applicable for a continuous primary ion beam, and for thick targets that are not transparent to the primary ion beam. The solution preserves the good mass resolution of the thin target setup, where the detection of primary ions with a PIN diode is used for a start trigger, reduces the background, and enables a wide range of samples to be analyzed.</p>
Simulation Files for Organic Contaminants and Atmospheric Nitrogen at the Graphene–Water Interface
<p>This data set provides files needed to run the simulations described in the manuscript entitled "Organic contaminants and atmospheric nitrogen at the graphene–water interface: A simulation study" using the molecular dynamics software NAMD and LAMMPS. The output of the simulations, as well as scripts used to analyze this output, are also included. The files are organized into directories corresponding to the figures of the main text and supplementary information. They include molecular model structure files (NAMD psf), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD or LAMMPS configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and some trajectories in dcd format (downsampled). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts. A modified LAMMPS C++ source file is also included.</p>
Riverine truly dissolved, colloidal, and operationally dissolved ferric iron, dissolved organic carbon, UV-vis absorbance at 254 and 412 nm, and specific UV absorbance (SUVA) at 254 nm
<p>This dataset is attached to the following article:</p> <p>Logozzo, L.A., Martin, J.W., McArthur, J., Raymond, P.A. Contributions of Fe(III) to UV-vis absorbance in river water: A case study on the Connecticut River and argument for the systematic tandem measurement of Fe(III) and CDOM. <em>Biogeochemistry</em> <strong>160</strong>: 17–33 (2022). https://doi.org/10.1007/s10533-022-00937-5</p> <p>This dataset includes concentrations of ferric iron, Fe(III), and dissolved organic carbon (DOC) along with UV-vis absorbance at 254 nm and 412 nm on the operationally dissolved size fraction (<0.22 μm), as well as the truly dissolved (<0.02 μm) and colloidal (0.02-0.22 μm) size fractions. Samples were collected every two weeks at five sites along the Connecticut River mainstem from 2018 to 2020 and at 7 sites in the Connecticut River watershed sampled once synoptically during the summer 2019.</p>
Chemical composition and sources of organic aerosol on the Adriatic coast in Croatia
<p>Air pollution studies are still scarce in some areas in Europe like around the Adriatic Sea. Source apportionment of the fine particulate (PM<sub>2.5</sub>) organic aerosol (OA) was conducted near Rogoznica, a small touristic settlement on the Eastern Adriatic coast of Croatia, near Lake Rogoznica (43.53° N, 15.95° E). Filter-based offline analyses of PM by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) were used to apportion OA to its sources. We quantified the contributions of fresh biomass burning OA (BBOA) and three oxygenated OA (OOA), denoted as background OOA (bkgOOA), summer OOA (SOOA), and sulfur-containing OOA (SC-OOA). The bkgOOA component correlates with anthropogenically influenced secondary inorganic aerosol constituents (e.g. sulfate) and dominates OA both during the warm and cold seasons (44%). It exhibits characteristics of regional SOA and there are indications that during the warm season SOA from wildfires could be substantial contributors. EESI-TOF measurements of the levoglucosan related ion showed a high correlation with the BBOA factor. Secondary OA has a similar molecular composition during the cold and warm seasons – in line with the large contribution of bkgOOA throughout the investigated seasons. SOOA comprises 19% of total OA and increases exponentially with the local temperature, consistent with SOA production by oxidation of biogenic emissions. In addition to biogenic precursors, other precursors (alkanes and aromatic) plausibly also contribute to the SOOA enhancement during the warm season. SC-OOA is a minor contributor to OA (6%) and is most likely linked to emissions from a close-by marine lake.</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.