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Label-Free Quantification (LFQ) LC-MS Data for the Profiling of Domain-specific VCP/p97 Interactions in Living Cells
<p>This deposit includes LFQ LC-MS data for tandem-IP samples from VCP-L278AbK and VCP-D592AbK crosslinking experiments, their VCP-L278BocK and VCP-D592BocK controls, and SpectroMine output files.</p>
Android Malware Family Labeling
<p>This repository includes VirusTotal scan reports of samples of two datasets used in the paper.</p> <p>(1) GPset-VT-Reports.zip: the first and second VT scans for the GPset samples.</p> <p>(2) 3rdset-VT-Reports.zip: the first and second VT scans for the 3rdset samples.</p> <p>(3)FusedApp-VT-Reports.zip: the VT scan for the fused (multi-family) samples.</p> <p>Each scan report is a JSON file generated by VirusTotal, including file metadata (e.g., hashes, size), certificate metadata (e.g., thumbprint), VirusTotal-specific data (e.g., submission time), and a list of detection labels assigned by various antivirus engines used to scan the file.</p>
Deep-sea observatories images labeled by citizen for object detection algorithms
<div>All information is available from the original publication page: <a href="https://doi.org/10.17882/101899">https://doi.org/10.17882/101899</a>.</div>
One-Step Photochemical Immobilization of Aptamer on Graphene for Label-Free Detection of NT-proBNP
<p>A novel photochemical technological route for one-step functionalization of a graphene surface with an azide-modified DNA aptamer for biomarkers is developed. The methodology is demonstrated for the functionalization of a DNA aptamer for an N-terminal B-type natriuretic peptide (NT-proBNP) heart failure biomarker on the surface of a graphene channel within a system based on a liquid-gated graphene field effect transistor (GFET). The limit of detection (LOD) of the aptamer-functionalized sensor is 0.01 pg/mL with short response time (75 s) for clinically relevant concentrations of the cardiac biomarker, which could be of relevance for point-of-care (POC) applications. The novel methodology could be applicable for the development of different graphene-based biosensors for fast, stable, real-time, and highly sensitive detection of disease markers.</p>
Sampled data for varifying the correctness of CHEN-AND (Labeled Dataset for Chinese and English Joint Author Name Disambiguation)
Open the record for dataset details and reuse information.
Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis
<p><strong>Please cite the following paper when using this dataset</strong>:</p> <p>N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)</p> <p> </p> <p><strong>Abstract</strong></p> <p>The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of <strong>500,153 Instagram posts about COVID-19 published between January 2020 and September 2024</strong>. This dataset contains Instagram posts in <strong>161 different languages</strong>. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.</p> <p><em><strong>For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.</strong></em></p> <p>The Instagram posts in this dataset are present in <strong>161 different languages</strong> out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)</p> <p>There are <strong>535,021 distinct hashtags in this dataset</strong> with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)</p> <p>The following is a description of the attributes present in this dataset</p> <ul> <li><em><strong>Post ID</strong></em>: Unique ID of each Instagram post</li> <li><em><strong>Post Description</strong></em>: Complete description of each post in the language in which it was originally published</li> <li><em><strong>Date</strong></em>: Date of publication in MM/DD/YYYY format</li> <li><em><strong>Language code</strong></em>: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API </li> <li><em><strong>Full Language</strong></em>: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API </li> <li><em><strong>Sentiment</strong></em>: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral</li> </ul> <p><strong>Open Research Questions</strong></p> <p>This dataset is expected to be helpful for the investigation of the following research questions and even beyond:</p> <ol> <li>How does sentiment toward COVID-19 vary across different languages?</li> <li>How has public sentiment toward COVID-19 evolved from 2020 to the present?</li> <li>How do cultural differences affect social media discourse about COVID-19 across various languages?</li> <li>How has COVID-19 impacted mental health, as reflected in social media posts across different languages?</li> <li>How effective were public health campaigns in shifting public sentiment in different languages?</li> <li>What patterns of vaccine hesitancy or support are present in different languages?</li> <li>How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?</li> <li>What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?</li> <li>How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?</li> <li>What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?</li> </ol> <p>All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets and Hierarchical Labels
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Multiclass Masked and labelled Dataset for citrus leave diseases.
<p>Data for multiclass semantic segmentation , it will be masked and labelled the many classes that are present in the leaves dataset</p>
GazeMining: A Dataset of Video and Interaction Recordings on Dynamic Web Pages. Labels of Visual Change, Segmentation of Videos into Stimulus Shots, and Discovery of Visual Stimuli.
<p><strong>Recording setup</strong><br> Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc".</p> <p><strong>Description of the dataset</strong><br> The dataset consists of following three subsets.</p> <p><em>1. Dataset_visual_change</em><br> The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files:</p> <ul> <li><site>.json: datacast</li> <li><site>.webm: video recording</li> <li><site>.features.csv: computer-vision features per observation</li> <li><site>.features_meta.csv: meta information about features</li> <li><site>.labels-l<X>.csv: labels of observations</li> <li><site>_meta.csv: meta information about recording</li> <li><site>_scroll_cache.csv: cache of estimated scrolling</li> <li><site>_scroll_cache_map.csv: mapping of observations to scroll cache entries</li> <li><site>_times.csv: timestamps of frames in the video recording</li> <li><site>_layer_pixels.csv: first row is the pixel count of root layer, second row is pixel count of all fixed elements</li> </ul> <p><em>2. Dataset_stimuli</em><br> Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and<br> the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as <participant>_<site>.csv file. For each shot, there are four further files:</p> <ul> <li><participant>_<site>_<shot>.png: stitched frame of the stimulus shot</li> <li><participant>_<site>_<shot>-blind.csv: frames from animations that are not contributing to the stitched frame</li> <li><participant>_<site>_<shot>-gaze.csv: gaze data (in stitched frame space)</li> <li><participant>_<site>_<shot>-mouse.csv: mouse data (in stitched frame space)</li> </ul> <p>The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in <layer_index>-<xpath>-meta.csv files. Furthermore, there are directories per layer, storing the discovered stimuli. Each discovered visual stimulus is represented by four files:</p> <ul> <li><stimulus_id>.png: stitched frame of the visual stimulus</li> <li><stimulus_id>-gaze.csv: gaze data (in stitched frame space)</li> <li><stimulus_id>-mouse.csv: mouse data (in stitched frame space)</li> <li><stimulus_id>-shots.csv: contained stimulus shots</li> </ul> <p><em>3. Dataset_evaluation</em><br> We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data.</p> <p><strong>Changelog</strong><br> [1.0.2] Add counts of layer pixels per participant.<br> [1.0.1] Change to CC0 license.<br> [1.0.1] Add labels of third annotator "l3".<br> [1.0.0] Initial release.</p>
FIG UR E 3 (a) Dated phylogeny of the genus Theodoxus constructed in BEAST based on COI, 16S and ATPα. Node labels denote divergence times in millions of years ago (Ma); node bars indicate the 95% credibility interval around these dates. Small squares at nodes indicate significant support of divergence events found with BEAST and other phylogenetic analyses (see Figures S2.1 and S2.2), as explained through the key. Where MOTUs (A–R) show conspecifics among a number of morphospecies, species names are given in order of their year of description. Morphospecies, incorporated from GenBank, where determination was potentially dubious are highlighted by an asterisk. Clades (C) and subclades (SC) are demarcated by dashed lines between MOTUs. (b) LTT plots indicating the build‐up of lineages in Theodoxus over geological time. Dashed lines surrounding the solid LTT lines indicate the 95% confidence intervals. Where intra‐ and interspecific diversity diverge, interspecific diversity is highlighted in blue and intraspecific diversity in red. Transitions in geological ages are highlighted by narrow grey lines, while the grey bar marks the period of pronounced glacial cycles (last 900 kyr) [Colour figure can be viewed at wileyonlinelibrary.com] in Contributions of biogeographical functions to species accumulation may change over time in refugial regions
FIG UR E 3 (a) Dated phylogeny of the genus Theodoxus constructed in BEAST based on COI, 16S and ATPα. Node labels denote divergence times in millions of years ago (Ma); node bars indicate the 95% credibility interval around these dates. Small squares at nodes indicate significant support of divergence events found with BEAST and other phylogenetic analyses (see Figures S2.1 and S2.2), as explained through the key. Where MOTUs (A–R) show conspecifics among a number of morphospecies, species names are given in order of their year of description. Morphospecies, incorporated from GenBank, where determination was potentially dubious are highlighted by an asterisk. Clades (C) and subclades (SC) are demarcated by dashed lines between MOTUs. (b) LTT plots indicating the build‐up of lineages in Theodoxus over geological time. Dashed lines surrounding the solid LTT lines indicate the 95% confidence intervals. Where intra‐ and interspecific diversity diverge, interspecific diversity is highlighted in blue and intraspecific diversity in red. Transitions in geological ages are highlighted by narrow grey lines, while the grey bar marks the period of pronounced glacial cycles (last 900 kyr) [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 3 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 3. Confocal projections of the longitudinal sections of the body wall musculature showing diagonal muscles (A–E) and fascias of the extracellular matrix (F, arrows), dorsal view. (A) Carinina sp., precerebral region; (B) Hubrechtella juliae, just behind mouth; (C) Cephalothrix cf. simula, just behind mouth; (D) Tetrastemma sp., middle body region; (E) Quasitetrastemma stimpsoni, middle body region; (F) Collarenemertes bimaculata, middle body region. Abbreviation: gp, gonopore. Scales: 50 µm.
Figure 5 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 5. Confocal projections of the transversal (A, B) and longitudinal (C–I) sections of the proboscis. Anterior (A–D, F, G) and middle (E, H, I) proboscis portions. (A, B), everted proboscis of Cerebratulus marginatus; (C) third proboscis region of Callinera sp.; (D) longitudinal and diagonal musculature in region of muscle cross (arrows) of Nipponomicrura uchidai; (E) bulb region of Emplectonema gracile; (F, G) anterior proboscis region of Ototyphlonemertes martynovi (F) and Ototyphlonemertes valentinae (G); (H, I) middle proboscis region of Ototyphlonemertes martynovi (H) and Ototyphlonemertes valentinae (I). Abbreviations: as, anterior sphincter; cm, circular musculature; dm, diagonal musculature; ge, glandular epithelium; icm, inner circular musculature; lm, longitudinal musculature; ls, longitudinal muscle strand; mc, muscle cross; pms, posterior muscle sheath. Scale: (A), 200 µm; (B–E, G), 50 µm; (F, H, I), 20 µm.
Figure 7 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 7. Confocal projections of the longitudinal sections of gonadal sac musculature. (A, B, E, F) mature ovaries of Emplectonema gracile (A), Antarctonemertes varvarae (B), Tetrastemma sp. (E), and Malacobdella grossa (F), ventral view; (C) immature testis of Antarctonemertes varvarae, ventral view; (D) gonopore of Tetrastemma sp., dorsal view. Abbreviation: gp, gonopore; lm, longitudinal body musculature. Scale: (A–E), 50 µm; (F), 100 µm.
Figure 2 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 2. Confocal projections of the longitudinal (A–D) and transversal (E) sections showing dermal musculature. (A, B) Cerebratulus marginatus, just behind mouth, dorsal view (A), precerebral region, dorsal view (B); (C) Lineus alborostratus, just behind mouth, dorsal view; (D, E) Hubrechtella juliae, brain region, dorsal view (D), transversal section through the brain region showing connection of the dermal and outer circular muscles (arrows) (E). Abbreviations: br, brain; cdm, circular dermal muscles; em, epidermal muscle processes; lm, longitudinal musculature; pr, proboscis. Scales: 50 µm.
Figure 6 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 6. Schematic diagrams of the body wall musculature (A–C) and proboscis musculature (D–F). (A) Tubulanid palaeonemerteans; (B) heteronemerteans; (C) hoplonemerteans; (D, D̓) palaeonemerteans (D, Cephalothrix; D̓, Carinoma); (E) Ototyphlonemertes valentinae; (F) lineid nemerteans (only outer circular and diagonal musculature). Abbreviations: ddm, diagonal dermal muscles; dm, diagonal muscles; icm, inner circular musculature; ilm, inner longitudinal musculature; ldm, longitudinal dermal muscles; lm, longitudinal musculature; ls, longitudinal muscle strand; mc, muscle cross; mcm, middle circular musculature; ocm, outer circular musculature; olm, outer longitudinal musculature; rem, radial epidermal muscles.
Figure 4 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 4. Confocal projections of the longitudinal (A–H, J) and transversal (I) sections of anterior proboscis region. (A, B) Inner circular musculature of Carinoma mutabilis (A) and Lineus alborostratus (B); (C) longitudinal, diagonal and outer circular musculature of Carinina sp.; (D) anterior proboscis bulb of Carinomella sp.; (E, F) interweaving of the longitudinal musculature of Micrura kulikovae (E) and Malacobdella grossa (F); (G, H, J) longitudinal and diagonal musculature of Cephalothrix sp. (G), Carinoma mutabilis (H), and Micrura kulikovae (J); (I) everted proboscis of Carinoma mutabilis (muscle crosses indicated by arrows; high magnification image shows muscle cross). Abbreviations: dm, diagonal muscles; icm, inner circular musculature; lm, longitudinal musculature; ocm, outer circular musculature. Scales: 50 µm.
Figure 1 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 1. Confocal projections of the longitudinal (A–G) and transversal (H, I) sections of the body wall musculature. (A) Epidermal muscle meshwork immediately posterior to the mouth in Carinoma mutabilis, ventral view; (B) diagonal epidermal muscle fibres (arrows), pigment ring region of Callinera sp., dorsal view; (C) radial epidermal muscles (arrows) of Carinina sp., anterior region, ventral view of body margin; (D–F) intraganglionic radial muscles (arrows) in the brain of Carinoma mutabilis (D), in the lateral nerve cords of Cephalothrix cf. simula (E), and in the head of Cephalothrix sp., larva (F), all dorsal view; (G–I) Cerebratulus marginatus, radial epidermal muscles, just behind mouth, ventral view (G), fragment of transversal section through the intestine region showing epidermal muscles (arrows) (H), fragment of transversal section through the foregut region showing radial muscles (arrows) (I). Abbreviations: br, brain; dm, dermal muscles; dvm, dorsoventral muscles; fg, foregut; ep, epidermis; ilm, inner longitudinal musculature; lm, longitudinal musculature; ln, longitudinal nerve cord; mcm, middle circular musculature; pr, proboscis. Scales: 50 µm.
FIGURE 13. Adult female Eriococcus thymelaeae Newstead. For labels, see Fig. 1 in A new species of Eriococcus (Hemiptera: Sternorrhyncha: Coccoidea: Eriococcidae) and redescriptions of Eriococcus roboris Goux and E. thymelaeae Newstead, with a key to the Eriococcus species on Quercus in the western Palaearctic
FIGURE 13. Adult female Eriococcus thymelaeae Newstead. For labels, see Fig. 1, except apb = anal plate bar.
FIGURE 6 in Plant structure ontology: How should we label plant structures with doubtful or mixed identities? *
FIGURE 6. Visual morphospace (character cladogram) for ovary structure at the mid-locular level of the Zingiberaceae and Costaceae (modified from Kirchoff et al. 2007). A visual morphospace was created by assembling standardized photographs of homologous parts and arranging them according to similarity. The groups are hierarchical, and are not associated with terminology. Both intermediate structures and structures of unknown identity can easily be accommodated in the morphospace. A. Alpinia calcarata (Zingiberaceae). B. Etlingera elatior (Zingiberaceae). C. Riedelia sp. (bilocular, Zingiberaceae). D. Riedelia sp. (trilocular, Zingiberaceae) E. Dimerocostus strobilaceus (Costaceae). F. Hedychium coronarium (Zingiberaceae). G. H. gardnerianum (Zingiberaceae). H. H. flavescens (Zingiberaceae). I. Zingiber zerumbet (Zingiberaceae). J. Costus dubius (Costaceae). K. Kaempferia atrovirens (Zingiberaceae). L. Scaphochlamys kunstleri (Zingiberaceae). M. Globba marantiana (Zingiberaceae).
FIGURE 4 in Plant structure ontology: How should we label plant structures with doubtful or mixed identities? *
FIGURE 4. Developmental aspects of submerged plant parts (lacking roots) of Utricularia foliosa (Lentibulariaceae). A, B. Growing watershoot tips with conspicuously curved indeterminate shoot apical meristems (SAM). The meristematic tips of the lobes, which continue to divide, are less curved than the SAM of the watershoots. Sessile two-celled hairs are visible on the convex and concave sides of the young watershoots. 1–5 = "leaves" = outgrowths along watershoot flank, each one dividing to form two determinate lobes. X = inflorescence primordium. Scale bars = 50 µm (A) and 100 µm (B). C. Determinate ends of leaf tip (arrowhead) and primary leaflet tips. The curved tips elongate considerably by precocious differentiation before ceasing growth. Scale bar = 500 µm. D. Racemose inflorescence apex forming bract (B) primordia, with floral apices (arrowhead) in their axils. Note presence of slightly curved tips of leaf primordia (L). Scale bar = 100 µm. Fig. 4A modified from Sattler & Rutishauser (1990).
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.