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Training dataset for label-free microfluidic droplet classification in single cell functional assays
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Polarisation camera dSTORM datasets of actin in fixed HeLa cells labeled with phalloidin-Alexa Fluor 488
<p>Polarisation camera dSTORM dataset of the actin of fixed HeLa cells labeled with phalloidin-Alexa Fluor 488.</p> <p><strong>Image acquisition was performed as follows:</strong></p> <p>Imaging was performed on a widefield microscope equipped with a polarisation camera (CS505MUP, Thorlabs). The sample was excited using a 488 nm laser at quasi-TIRF, with a measured power density at the image plane of <span>5.04 kW/cm^2</span>. A single long-pass dichroic (Di02-R488, Semrock) was used to seperate fluorescence from the excitation. The emission was filtered using a long-pass (BLP01-488R, Semrock) and a bandpass filter (FF01-582/64, Semrock) before detection. An exposure time of 30 ms was used.</p> <p><strong>Samples were prepared as follows:</strong></p> <p><span>Cell culture: </span>HeLa TDS cells were cultured in DMEM (Gibco, Invitrogen) supplemented with 10 % Fetal Bovine Serum (FBS, Life Technologies), 1 % penicillin/streptomycin (Life Technologies), and 1 % glutamine (Life Technologies) at 37 °C + 5 % CO_2. Cells were periodically tested for mycoplasma contamination and passaged 3 times per week. Cells were plated at low density on high-precision glass coverslips (MatTek, P35G-0.170-14-C) 1 day prior to fixation for dSTORM experiments.</p> <p><span>Labeling:</span> Cells were simultaneously fixed and permeabilized in cytoskeleton buffer (CBS, 10 mM MES, 138 mM KCl, 3 mM MgCl_2, 2 mM EGTA, 4.5 % sucrose w/v, pH 7.4), + 4 % paraformaldehyde (PFA) and 0.2 % Triton for 6 minutes at 37 °C, and further fixed in CBS + 4 % PFA for 14 minutes at 37 °C. Post-fixation, cells were washed x3 in PBST (PBS supplemented with 0.1 % Tween) and permeabilized a second time in PBS + 0.5 % Triton for 5 minutes at room temperature. The samples were then washed 3 times in PBST and blocked for 30 minutes in 5 % BSA. Cells were washed x3 in PBST and then incubated with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen, 1:50 in PBS) for 1 h in the dark, followed by x2 washes in PBS. Prior to dSTORM imaging, PBS was replaced with dSTORM imaging buffer (base buffer consisting of 0.56 M glucose, 50 mM Tris (pH 8.5), and 10 mM NaCl supplemented with 5 U/mL pyranose oxidase (Sigma, P4234), 10 mM cysteamine (Sigma, 30070), 40 µg/mL catalase (Sigma, C100) and 2 mM cyclooctatetraene (Sigma, 138924).</p>
Polarisation camera dSTORM datasets of actin in fixed HeLa cells labeled with phalloidin-Alexa Fluor 647
<p>Polarisation camera dSTORM dataset of the actin of fixed HeLa cells labeled with phalloidin-Alexa Fluor 647.</p> <p><strong>Image acquisition was performed as follows:</strong></p> <p>Imaging was performed on a widefield microscope equipped with a polarisation camera (CS505MUP, Thorlabs). The sample was excited using a 638 nm laser at quasi-TIRF, with a measured power density at the image plane of 3.51 kW/cm^2. A multiband dichroic (Di03-R405/488/561/635-t1, Semrock) was used to seperate fluorescence from the excitation. The emission was filtered using a long-pass filter (BLP01-635R, Semrock) before detection. An exposure time of 30 ms was used.</p> <p><strong>Samples were prepared as follows:</strong></p> <p>Cell culture: HeLa TDS cells were cultured in DMEM (Gibco, Invitrogen) supplemented with 10 % Fetal Bovine Serum (FBS, Life Technologies), 1 % penicillin/streptomycin (Life Technologies), and 1 % glutamine (Life Technologies) at 37 °C + 5 % CO_2. Cells were periodically tested for mycoplasma contamination and passaged 3 times per week. Cells were plated at low density on high-precision glass coverslips (MatTek, P35G-0.170-14-C) 1 day prior to fixation for dSTORM experiments.</p> <p>Labeling: Cells were simultaneously fixed and permeabilized in cytoskeleton buffer (CBS, 10 mM MES, 138 mM KCl, 3 mM MgCl_2, 2 mM EGTA, 4.5 % sucrose w/v, pH 7.4), + 4 % paraformaldehyde (PFA) and 0.2 % Triton for 6 minutes at 37 °C, and further fixed in CBS + 4 % PFA for 14 minutes at 37 °C. Post-fixation, cells were washed x3 in PBST (PBS supplemented with 0.1 % Tween) and permeabilized a second time in PBS + 0.5 % Triton for 5 minutes at room temperature. The samples were then washed 3 times in PBST and blocked for 30 minutes in 5 % BSA. Cells were washed x3 in PBST and then incubated with Alexa Fluor™ 647 Phalloidin (A22287, Invitrogen, 1:50 in PBS) for 1 h in the dark, followed by x2 washes in PBS. Prior to dSTORM imaging, PBS was replaced with dSTORM imaging buffer (base buffer consisting of 0.56 M glucose, 50 mM Tris (pH 8.5), and 10 mM NaCl supplemented with 5 U/mL pyranose oxidase (Sigma, P4234), 10 mM cysteamine (Sigma, 30070), 40 µg/mL catalase (Sigma, C100) and 2 mM cyclooctatetraene (Sigma, 138924).</p>
Data from "Single-molecule fluorescence multiplexing by multi-parameter spectroscopic detection of nanostructured FRET labels"
<p>Source data for all figures; full table of all nucleic acid sequences used; Supplementary Video 1 showing rotating view of Fig. 4e.</p>
FIGURES 1–8. Agaristinae adults and labels. 1 in Rediscovery of the genus Saigonita Kiriakoff (Lepidoptera, Noctuidae, Agaristinae), with an updated checklist of Agaristinae from China
FIGURES 1–8. Agaristinae adults and labels. 1, Saigonita paradoxa, holotype, Saigon (Ho Chi Minh City), S. Vietnam, upperside; 2, ditto, underside; 3, ditto, labels; 4, Saigonita paradoxa, SY1, upperside; 5, ditto, underside; 6, Saigonita paradoxa, En318493, upperside; 7, ditto, underside; 8, Pemphigostola synemonistis, after Strand (1930). Scale for figures 1, 2, 4–7.
Labeling Efficiency Datasets
<p>Localization Microscopy Datasets from this publication: https://dx.doi.org/10.1038/s41592-024-02242-5. Archive contains all relevant .hdf5 datasets.</p>
Microstructure images, ilastik labels and probability maps.
<p>Microstructure images of several Al alloys reinforced with quasicrystals. Images were obtained using a Zeiss Axio Imager.A1m with AxioCam ERc 5s. Original images were enhanced using FIJI and analysed using ilastik. Included is the ilastik training set, labels used and probability maps.</p>
Directed labeled multigraphs and directed labeled multigraphs enriched with information about which graph patterns match a graph and how they match a graph
<p>This dataset contains all directed labeled multigraphs that consist of one or two triples and that were generated from a set of 5 terms.</p> <p>The graphs are represented as RDF data in ntriples format.</p> <p>The graphs 1-125 consist of a single triple. The graphs 126-4625 consist of two triples.</p> <p>Each graph also exists in an enriched version, where each graph pattern that matches the graph is made explicit in the graph and it is also made explicit how it matches the graph.</p> <p>Enrichment means that for each graph pattern that matches a graph, a node is introduced that is labeled with the name of that graph pattern. Each term that occurs in that graph pattern is linked to that graph pattern node with an edge labeled "occurs-in". For each term t in the graph that can be bound to a variable v according to a match of that graph pattern an edge will be created from the node t to the graph pattern node where the edge is labeled with "bound-to-v".</p> <p>The generation of the dataset and the enrichment procedure is described in more detail in a paper that is currently under double-blind submission. We will link to the paper once it is accepted for publication.</p> <p> </p> <p>Example: the graph #100 (file 100.nt)</p> <p><http://ex.org/term/4> <http://ex.org/term/5> <http://ex.org/term/5> .</p> <p>Example: the graph #2000 (file 2000.nt)</p> <p><http://ex.org/term/2> <http://ex.org/term/2> <http://ex.org/term/3> .<br><http://ex.org/term/2> <http://ex.org/term/3> <http://ex.org/term/1> .</p> <p>Example: the enriched graph #100 (file 100-extended.nt)</p> <p><http://ex.org/term/4> <http://ex.org/term/5> <http://ex.org/term/5> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-v1-t5-v2> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-t5-v2> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v2> <http://ex.org/pattern/p-v1-t5-v2> .<br><http://ex.org/term/4> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-v1-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-t4-v1-v1> .<br><http://ex.org/term/4> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-t5-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-t5-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-t4-t5-v1> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-v1-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-t4-v1-t5> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-t4-v1-t5> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-v1-t5-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-t5-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-v2-v2> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v2> <http://ex.org/pattern/p-v1-v2-v2> .<br><http://ex.org/term/5> <http://ex.org/pattern/occurs_in> <http://ex.org/pattern/p-v1-v2-t5> .<br><http://ex.org/term/4> <http://ex.org/pattern/bound-to-v1> <http://ex.org/pattern/p-v1-v2-t5> .<br><http://ex.org/term/5> <http://ex.org/pattern/bound-to-v2> <http://ex.org/pattern/p-v1-v2-t5> .<br><br></p> <p>For each graph pattern exists a CSV file that lists the IDs of all graphs that are matched by that pattern.For example, the file target-p-t3-t4-v1=v1-t1-t2-graph_size_2.csv lists all those graphs that are matched by the pattern t3-t4-v1=v1-t1-t2.</p> <p>Finally, there are four JSON files:</p> <p>plain-graphs_of_size_1.json, plain-graphs_of_size_2.json, enriched-graphs_of_size_1.json, enriched-graphs_of_size_2.json </p> <p>These files contain the same graphs that are also stored in the .nt-files, but allow to conveniently read in a set of graphs at once.</p> <p> </p>
Dataset : Micromolar concentration affinity study on a benchtop NMR spectrometer with secondary 13C labeled hyperpolarized ligands
<p><strong>Data of the dDNP decays for 13C drug screening</strong></p> <p><strong>Drug discovery Data recap</strong></p> <p> </p> <p><strong>Samples</strong></p> <p>Sample 2 : 44mM of Ac-L30 (N-Acetyl [1-<sup>13</sup>C]-6 amino-2-naphthoic acid) in 60/30/10 DMSO/D2O/H2O with 25 mM Tempol</p> <p>Sample 4 (in fact sample 3 in Topspin and according to OC figures) : 44mM of Ac-L08 (N-Acetyl [1-<sup>13</sup>C]-glycine) in 60/30/10 DMSO/D2O/H2O with 25 mM Tempol</p> <p> </p> <p><strong>Dissolution Ac-L30 </strong></p> <p><em>Sample : 600 µM Sample 2 without and with 2 µM HSA</em></p> <p><em>Topspin Folder : 20210414-DrugScreening</em></p> <ul> <li>Dissolution fragment : 1</li> <ul> <li>D1 5 sec</li> <li>Int between 167.2 - 168.3 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 11.88 (10.84, 12.91)</li> <li> a = 1.017 (0.967, 1.066)</li> <li> d = 0.0005482 (-0.006744, 0.007841)</li> </ul> <li>TE : 2</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <li>Dissolution fragment + HSA : 3</li> <ul> <li>D1 5 sec</li> <li>Integration between 167.9 - 168.1 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 6.339 (5.57, 7.109)</li> <li> a = 1.027 (0.9665, 1.088)</li> <li> d = -0.007015 (-0.01521, 0.001185)</li> </ul> <li>TE : 4</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <ul> <li>Code used to plot : Ac-L30.m</li> </ul> </ul> <p> </p> <p><strong>Dissolution Ac-L30 </strong></p> <p><em>Sample : 600 µM Sample 4 without and with 2 µM HSA</em></p> <p><em>Topspin Folder : 20210414-DrugScreening</em></p> <ul> <li>Dissolution fragment : 5</li> <ul> <li>D1 2,5 sec</li> <li>Int between 168.6 - 169 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 29.25 (29.05, 29.46)</li> <li> a = 1.019 (1.015, 1.023)</li> <li> d = -0.001389 (-0.002239, -0.0005388)</li> </ul> <li>TE : 6</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <li>Dissolution fragment + HSA : 11</li> <ul> <li>D1 2,5 sec</li> <li>Integration between 168.6 - 169 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 29.73 (29.57, 29.88)</li> <li> a = 0.9938 (0.9905, 0.9971)</li> <li> d = -0.0003466 (-0.0007762, 8.314e-05)</li> </ul> <li>TE : 12</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <ul> <li>Code used to plot : Ac-L08.m</li> </ul> </ul>
Coastal Satellite Image Segmentation (Water and Land) Labels: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)
<p>Contained here are jpegs containing coastal RGB satellite images along with a water vs. land mask. Each image is 256 pixels by 256 pixels. </p> <p>Geographic scope: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)</p> <p>Temporal range: 1984 to 2022</p> <p>Satellites: Landsat 5, 7, 8 and Sentinel-2</p> <p>All images were downloaded from Google Earth Engine using CoastSat download tools.</p> <p>The datasets are arranged into 'train', 'val', and 'test' folders. Within each of those folders are two folders 'a' and 'b'. 'a' contains the images (RGB), whereas 'b' contains the labels (land vs. water mask).</p> <p>All images were augmented with the four following augmentations: horizontal flip, vertical flip, 90 degree clockwise rotation, 90 degree counterclockwise rotation, and a horizontal+vertical flip. </p> <p>For training a new segmentation model, it is advised to not do any of these rotational or flip augmentations since they have already been performed. Instead, possibly experiment with other augmentations like introducing noise into the imagery.</p> <p>These images were used to train an image-to-image translation generative adversarial network. The code and model weights (generator and discriminator) are available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>To get to the files locally, you can download the .zip from Zenodo and then unzip the .zip file.</p>
Dataset Labeled For Inciting Speech
<p>This dataset is related to the paper "Understanding Inciting Speech As New Malice." The paper recently got accepted at IEEE Transactions on Computational Social Systems. Please cite the paper while using the dataset. </p>
ATELIER. Self-Labelling dynamic Loss function training data
<p>This dataset contains all the statistics collected during the training of multiple Neural Networks, which uses the following architecture technologies:</p> <p>Siamese Neural Networks, Dynamic loss function, modified during the training through Reinforcement learning and a 'Curriculum-Learning' cycle in between training cycles. The goal of the Models is to correctly identify anomalies in the QoE of real-time videos through the network KPIs.</p> <p>The dataset contains 3 type of experiment with different levels of model complexity.</p> <p>Please refer to the associated github repository and the published paper for a detailed description of both the dataset and the infrastructure that generated the dataset.<br>Journal paper title: "ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning"</p>
The Sounds of Home (labels only)
<p>The Household Sounds dataset consists of recordings from 14 microphones, each housed in its own folder. Here you will find only the automatically generated predictions. You can find the complete dataset at https://zenodo.org/records/12737915</p>
FIGURES 1–8. Pyrgodesmus obscurus Pocock, 1892, male syntypes. 1. Original labels. 2 in Identity of the millipede genus Pyrgodesmus Pocock, 1892, the type genus of the family Pyrgodesmidae (Diplopoda, Polydesmida)
FIGURES 1–8. Pyrgodesmus obscurus Pocock, 1892, male syntypes. 1. Original labels. 2. Both syntypes pierced with glass minutiae, shown relative to the length of an insect pin. 3. Habitus of a syntype, lateral view (after Pocock (1892)). 4. Picture of the same, lateral view. 5. Body rings 5–7 and gonopods in situ, ventral view. 6. Body rings 6–9 and gonopods in situ, ventrolateral view. 7 & 8. Enlarged gonopods in situ, ventral and ventrolateral views, respectively. Figs 1–3, 7 and 8 not to scale. Images courtesy Dmitry Telnov.
FIGURE. Neotype of Euphorbia leandriana Boiteau, Boiteau s.n. (P, specimen formerly in spiritu), designated in the present paper. A, B. details of the specimen; C. specimen with copy of the original spirit label (lost), likely by J. Bosser. Credits: Muséum national d'histoire naturelle, Paris (A–C). in Novelties in Malagasy Euphorbia (Euphorbiaceae)
FIGURE. Neotype of Euphorbia leandriana Boiteau, Boiteau s.n. (P, specimen formerly in spiritu), designated in the present paper. A, B. details of the specimen; C. specimen with copy of the original spirit label (lost), likely by J. Bosser. Credits: Muséum national d'histoire naturelle, Paris (A–C).
Wikipedia Articles with Quality label FA, GA, A, B and C
<p>This dataset contains a filtered collection of good quality Wikipedia Articles:</p> <p>* FA: Featured Articles</p> <p>* GA: Good Articles</p> <p>* Quality lables A, B and C</p>
FIGURE 9. Specimen labeled A in Acianthera bilobulata (Orchidaceae: Pleurothallidinae), a new species from Southwestern Ecuador
FIGURE 9. Specimen labeled A. pubescens from Loja, Vilcabamba. Plate by B.J. Zambrano based on Zambrano s.n.
Retention fraction of 15N-labelled deposited ammonium and nitrate in forests
<p>The impacts of enhanced nitrogen (N) deposition on global forest carbon (C) sink and other ecosystem services may depend on whether N is deposited in reduced (mainly as ammonium) or oxidized forms (mainly as nitrate) and the subsequent fate of each. However, the fates of the two key reactive N forms and its contribution to forest C sink is unclear. W<span><span>e conducted ecosystem-scale paired <sup>15</sup>N-labelling experiments in nine forests across China to quantify N retention fractions for both deposited ammonium and nitrate, including tropical and sub-tropical forests for the first time. By combining these results with four previous experiments from temperate Europe and North America, here we show that total ecosystem N retention is similar for ammonium and nitrate, but plants consistently take up more of the labelled nitrate than ammonium<sup> </sup> while soils retain more ammonium than nitrate. Nitrogen retention in plants and soils across sites is predicted by a combination of tree (NPP and woody biomass) and soil (organic layer mass and soil C/N ratios) variables. Greater proportions of deposited N are retained in N-limited ecosystems with low soil N availability and high soil C/N ratios. We estimate that N deposition-induced C sink in forests contributes more than 20% of the total terrestrial C sink. Although less N is deposited in oxidized than reduced state, their total contributions to the global forest C sink are approximately equal due to more efficient use by trees of the oxidized than the reduced form. Our study demonstrates differential fates of reduced and oxidized N deposition that improves current understanding of the C-N interaction in forests and indicates a greater C sink attributable to atmospheric N deposition than previous estimates.</span></span></p>
FIGURES 73–78. Scutellera holosericea Dohrn, 1863, a syntype and its labels. Fig. 73 in A revision of Scutellera (Hemiptera: Heteroptera: Scutelleridae)
FIGURES 73–78. Scutellera holosericea Dohrn, 1863, a syntype and its labels. Fig. 73, dorsal view; Fig. 74, left lateral view; Fig. 75, right lateral view; Fig. 76, ventral view; Fig. 77, head and fragment of left antenna mounted on card; Fig. 78, labels. Scales in mm. © ZMPA.
VTSet: Benchmarking Label Dynamics of VirusTotal Engines
<p>This is the dataset of paper "<a href="https://dl.acm.org/doi/abs/10.1145/3372297.3420013?casa_token=kjEVVowQXwwAAAAA:yso4l50ERdLPRQ01jr5k66lmYFDMclL2UELFqTjRRcZ2GuLvPjfX1oN0Jzj3m5kxw9DyJXZXtss">Benchmarking Label Dynamics of VirusTotal Engines</a>" in CCS 2020.</p> <p>Please refer to the paper for more information.</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.