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Proximity labeling of tau interactions in primary neurons and mouse brain
<p><span>Microtubule-associated protein tau is a central factor in Alzheimer's disease and other tauopathies. However, physiological functions of tau are unclear. Here, we used proximity labelling proteomics to chart functional tau interactomes in primary neurons and mouse brai<span>n <span>in vivo</span></span><span>. Here, we use proximity labelling with the biotin ligase BioID2 to map interactomes of tau in neurons. Data sets relate to mass spectrometry and protein identification of biotinylated proteins in primary neurons and in mouse brain after delivery of BioID2-tau fusion protein or BioID2 control protein by adeno-associated virus (AAV). Mouse brain samples are either from P35 wild-type mice with intracranial AAV delivery at P0 or from tau knockout mice at P60 after hippocampal delivery of AAV. Details on BioID2 fusion protein expression, biotin supplementation and sample extraction can be obtained in the associated publication.<br></span></span></p>
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Worrying confessions: A look at data safety labels on Android
<p>The Google Play Store recently introduced a data safety section in order to give users accessible insights into apps’ data collection practices. We analyzed the labels of 43,927 of the most popular apps. Almost one third of the apps with a label claims not to collect any data. But we also saw popular apps, including apps meant for children, admitting to collecting and sharing highly sensitive data like the user’s sexual orientation or health information for tracking and advertising purposes. To verify the declarations, we recorded the network traffic of 500 apps, finding more than one quarter of them transmitting tracking data not declared in their data safety label.</p> <p>This data set contains a dump of our database, including the top chart data and data safety labels from September 07, 2022, and the recorded network traffic.</p> <p>The analysis is available at our blog: <a href="https://www.datarequests.org/blog/android-data-safety-labels-analysis/">https://www.datarequests.org/blog/android-data-safety-labels-analysis/</a><br> The source code for the analysis is available on GitHub: <a href="https://github.com/datenanfragen/android-data-safety-label-analysis">https://github.com/datenanfragen/android-data-safety-label-analysis</a></p>
WhichDog: A crowdsourced dataset including candidate set-based labelling
<p>A dataset with crowdsourced labels for aggregation and supervised classification.<br> It contains 400 images of dogs from the Stanford Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). Images of dogs that belong to 32 different breeds (classes) are included. Annotators were asked to provide two types of labelling: full labelling (each labeler is allowed to provide a single label for each image) and candidate labelling (each labeler is allowed to provide a set of candidate labels for each image). It includes a total of 61227 annotations (30628 full and 30599 candidate) obtained from a set of 1028 different labelers.</p> <p>The labels were collected through the online crowdsourcing platform Amazon mTurk thanks to funds provided by the Basque Government through the Elkartek program (KK-2018/00071). The assignments were designed as sequences of 64 images that were given to the annotators. Each image in the sequence was provided together with a specific subset of possible labels (with the number of options ranging from 4 to 32), and a instruction for the annotator to perform a specific type of labelling (full or candidate). Each labeler performed at least one assignment. Not all the labelers completed the 64 annotations in their assignments.<br> <br> The file 'whichdog.zip' contains a folder ('images') with the 400 images of dogs, a text file ('breed_names.txt') that indicates the names of the different breeds and their assigned label (a number in the interval from 0 to 31) and a CSV file ('whichdog_all_annots.csv') that contains the information about the annotations. Each row of the CSV file represents a single annotation, and each column shows:<br> - image_id: ID number of the image.<br> - is_candidate: indicates whether the requested labelling is full (0) or candidate (1).<br> - labeler_id: ID number of the labeler.<br> - time: time employed by the labeler to perform the annotation.<br> - answer: label or set of labels provided by the labeler as annotation.<br> - options: subset of possible labels shown to the labeler.<br> - assignment_id: ID number of the assignment<br> - sequence_point: number that indicates the point of the sequence of images of the assignment in which the annotation was provided.<br> - class: ground truth label of the image.</p>
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
<p>Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.</p>
Autophagosomal/autolysosomal/lysosomal dynamics of FaDu and HGFb cells affected by autophagy modulators (dataset of dual labeling of autophagosomes and labeling lysosomes, confocal microscopy)
<p>In this dataset, the impact of autophagy modulators on the auophagosomes and lysosomes in FaDu and HGF cells was investigated. Experimental details are described in the accompanying paper. This dataset contains total 702 CZI confocal image Z-stacks.</p> <p><strong>Relevant paper</strong>: HANELOVA, Klara, RAUDENSKA, Martina, KRATOCHVILOVA, Monika, NAVRATIL, Jiri, VICAR, Tomas, BUGAJOVA, Maria, GUMULEC, Jaromir, MASARIK, Michal and BALVAN, Jan. Autophagy modulators influence the content of important signalling molecules in PS-positive extracellular vesicles. <em>Cell Communication and Signaling</em>. 24 May 2023. Vol. 21, no. 1, p. 120. DOI <a href="https://doi.org/10.1186/s12964-023-01126-z">10.1186/s12964-023-01126-z</a>.</p> <div> <div> <div> </div> </div> </div> <p><strong>Model cell lines</strong></p> <p> </p> <p>The cell line FaDu (HTB-43TM), derived from a squamous cell carcinoma (SCC) of the hypopharynx, and the human gingival fibroblast cell line HGF (derived from the histologically normal gingival biopsy) were used in this study. The authenticated cell lines were purchased from the American Type Culture Collection (ATCC; Manassas, Virginia, USA) within the last five years. </p> <p><strong>Autophagy modulation</strong></p> <p>For autophagy modulation, FaDu cells were treated for 24 h with 5nM bafilomycin A1 (Sigma-Aldrich, B1793), 50 µM of hydroxychloroquine sulphate (Sigma-Aldrich, H0915), 100 µM of Cpd18 (Calbiochem), 50 nM of autophinib (Sigma, SML2632), 10 µM of EACC (MedChemExpress), 200 nM of rapamycin (Sigma-Aldrich, R0395), 3 nM of Torin-1 (MedChemExpress), or 30 nM of NVP-BEZ235 (MedChemExpress). To induce starvation, cells were cultured in DMEM F12 without glutamine and without FBS (Biosera). Modulation of autophagy did not reduce the viability of FaDu cells.</p> <p><strong>Conditioned media preparation</strong></p> <p>see details in the accompanying paper</p> <p><strong>Fluorescence Microscopy</strong><br>The autophagosomal/autolysosomal/lysosomal dynamics of affected cells were observed using the combination of PremoTM Autophagy Tandem Sensor (P36239, Invitrogen) with the far-red emitting LysoTracker® Deep Red (L12492, Invitrogen) (Ex 647 nm/Em 668 nm). By combining acid-sensitive Emerald GFP (Ex 488 nm/Em 509 nm) with acid-insensitive TagRFP (Ex 555 nm/Em 584 nm) in the PremoTM kit, autophagosomes and autolysosomes labelling (yellow and red, respectively) is possible.<br>Immediately after transduction with 12 µl PremoTM Autophagy Tandem Sensor/2ml cell suspension, cells were seeded at 5 × 10<sup>5</sup> into 35-mm glass-bottomed gelatin-coated dishes (Ibidi, μ-Dish 35 mm, high Glass Bottom) and cultured for 48 h to equilibrate expression levels. Subsequently, cells were exposed to the selected agents for 6, 12, 24 and 48 hours before imaging. LysoTracker® Deep Red staining was performed 1 h before imaging. <br>To monitor the uptake of isolated PS-EVs by fibroblasts, we stained EVs with PKH67 (Sigma, PKH67GL) and then removed the remaining dye using Exosome Spin Columns (MW 3000) (Thermo Scientific, #4484449). The stained EVs were then suspended in 400 ul of cultivation medium and added to HGFB cells growing for 24 h in Ibidi µ-Slide I Luer (Ibidi, 80176). Image acquisition was performed 24 hours after EVs addition. 1 µl of 1 µg/ml of Hoechst 33342 (Enzo) (Ex 350 nm/ Em 461 nm) nuclear stain was added 1 hour before imaging.<br>To determine the viability of the cell population prior to isolation of EVs, cells were left in a culture dish with 1 ml of culture medium to which propidium iodide (Sigma-Aldrich) and Hoechst 33342 were added 45 minutes before capturing. <br>To maximize the possibility of comparison between samples, all samples (from a single cell line) were captured on the same day in a single run using the same microscope settings. For each time and each treatment, 10–12 fields of view were captured from randomized sites of the culture dish. Epifluorescent microscopy images and confocal microscopy images were acquired using Laser scanning confocal microscope Zeiss LSM 880 with AiryscanFast module (Carl Zeiss Inc.) using a C-apochromat 40x/1.20 W and C-Apochromat 63 /1.20 W. LysoTracker® Deep Red was excited HeNe 633 nm solid-state laser and emitted light was detected at 638–759 nm. Emerald GFP was excited 488 nm ArgonRemote laser, and emitted light was detected at 493–576 nm. TagRFP was excited DPSS 561 nm laser, and emitted light was detected at 570–650 nm. Hoechst 33342 was excited with a 405 nm solid-state laser, and emitted light was detected at 410–508 nm. Fluorescence images were acquired by the transmitted light detector. <br>Images were analyzed using ImageJ software and custom MATLAB software developed in our laboratory. The analysis process consists of the segmentation of cells from the background and extraction of the intensity of fluorescence channels (TagRFP, GFP, LysoTracker) inside cells. For segmentation, a thresholding-based method was used, where a manually selected threshold was applied. Segmentation was applied to the image created as the sum of all fluorescence channels to achieve segmentation independent of the intensity of individual channels. To achieve better segmentation without noisy pixels, fluorescence images were preprocessed with median filter (7x7) and Gaussian filter (standard deviation 1); additionally, binary segmentation was post-processed with morphological closing and removal of small binary connected components (<5000px). For intensity extraction, the mean value of segmented cell pixels was used for each field of view. Besides individual fluorescence channels, the mean colocalization of TagRFP and GFP was calculated with a pixel-wise multiplication of TagRFP and GFP channels.</p> <p><strong>File Naming</strong></p> <p>CZI images are organised in folders according to cell line, treatment time, and treatment. Names include magnification, cell line measured, treatment, and time of treatment,</p> <blockquote> <p>40x_FADU_BAF_12h_BF_4.czi</p> </blockquote> <p>The abbreviations used for treatments are as follows: BAF, bafilomycin A1; HCQ, hydroxychloroquine sulphate; Cpd18; APB, autophinib; EACC; RAPA, rapamycin; TOR1, Torin-1; BEZ, NVP-BEZ235; starv, starvation; </p>
An in vivo stable isotope labeling method to investigate individual matrix protein synthesis, ribosomal biogenesis, and chondrocyte proliferation in murine articular cartilage
<p>These experiments have used a stable-isotope method using <em>in vivo</em> deuterium oxide labeling and mass spectrometry to measure protein concentration, protein half-life, cell proliferation, and ribosomal biogenesis in a single sample of murine articular cartilage. We hypothesized that a 60-day labeling period would capture age-related declines in cartilage matrix protein content, protein synthesis rates, and chondrocyte proliferation. Knee cartilage was isolated from 25- and 90-week-old female C57BL/6J mice treated with deuterium oxide for 15, 30, 45 and 60 days. We measured protein abundance and half-lives using high resolution accurate mass spectrometry (HRAM) and d2ome data processing software. </p>
Standardizing ID-Labels for seaweed samples used for chemical composition analyses and refinery processes in Nordic and European research projects
<p><strong>Introduction</strong></p> <p>Seaweed samples can be divided into two groups:</p> <ol> <li>Small samples (½-3 kg wet weight (ww)) often used for chemical content analyses including seasonal variation and testing different cultivation conditions or preliminary lab scale experiments on storage, extraction, separation, fermentation, etc.</li> <li>Larger samples (>3 kg ww) for lab- or pilot scale experiments on storage, extraction, separation, fermentation, etc.</li> </ol> <p>Seaweed samples will always have the following information-tracks:</p> <ol> <li><strong>Sample Code: </strong>A ID containing the most important information and the sample code will follow the sampled biomass from harvest to final research results.</li> <li><strong>Seaweed Processing Code:</strong> The sample code will be extended with 8 digits and 1 letter if processing of biomass occurs.</li> <li><strong>Batch Number:</strong> A code describing details about the harvest and origin of the seaweed.</li> <li><strong>Sample Overview:</strong> An Excel file describing all details about the sample: first <strong>sample code</strong>, then species, grinding, freezing/drying specifications, seeding and harvesting information, planed aim of the sample (e.g. polysaccharides), place stored, seaweed processing details, analyse results, etc. Maintained by the sample provider.</li> </ol>
Proteomics LC-MS/MS test dataset for protein quantitation via stable isotope labelling
<p>The provided mzML file can be used as a test dataset for protein identification and quantitation software. It was generated from human embryonic kidney (HEK) cells that were either unlabelled or labelled with heavy SILAC (K6R6, unimod accession 188, PSI-MS Name: "Label:13C(6)"). Apart from different labelling, the HEK cells were kept in exactly the same conditions and harvested simultaneously. Light and heavy labelled proteins from HEK cell lysate were mixed in a certain ratio, digested with Trypsin and measured on a ThermoFisher QExactive mass spectrometer. A more detailed description on the generation of the dataset will soon be accessible at PRIDE.</p> <p>The provided mzML file has been converted from Thermo RAW and slightly modified via msConvert (ProteoWizard). To reduce the filesize and to speed up analysis, it has further been filtered to contain only the data measured between 2,000 sec and 3,000 sec of the original LC-MS/MS run.</p>
Generation of fluorescently labelled OP9 stromal cells for growing patient leukemic cells
<p>We added fluorescent labels to OP9 stromal cells to allow us to easily distinguish stromal cells from primary leukemia cells in co-culture growth conditions.</p>
A region-wide, multi-year set of crop field boundary labels for Africa
<div> <div> <p>Data resulting from a project undertaken to generate a comprehensive set of crop field boundary labels throughout the continent of Africa, representing the years 2017-2023. The project was funded by the <a title="" href="https://lacunafund.org/">Lacuna Fund</a>, and led by <a title="" href="https://farmerline.co/">Farmerline</a>, in collaboration with <a title="" href="https://spatialcollective.com/">Spatial Collective</a> and the <a title="" href="agroimpacts.info">Agricultural Impacts Research Group</a> at <a title="" href="https://www.clarku.edu/departments/geography/">Clark University</a>.</p> <p>Please refer to the technical report in the accompanying repository for more details on the methods used to develop the dataset, an analysis of label quality, and usage guidelines.</p> </div> </div>
VMXi Classification Dataset: Micrographs of Protein Crystallisation Experiments with Labels of Experimental Outcomes
<p>The VMXi Classification Dataset consists of images of protein crystallisation experiments collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the VMXi experimental facility at Diamond Light Source, UK. These images were used to train the CHiMP (Crystal Hits in My Plate) Classifier network that classifies images into categories of experimental outcome.</p> <ul> <li>The directory named "VMXi_Classification_Images", consists of 18,782 JPEG images with a resolution of 3376 × 2704 pixels. </li> <li>13,951 of these images are associated with a label describing the experimental outcome depicted in the images. The labels are Clear, Crystals, Precipitate or Other.</li> <li>The file "VMXi_Classification_Train.csv" contains filenames and labels for the 11,161 images in the training set used for the CHiMP Classifier network.</li> <li>The file "VMXi_Classification_Validation.csv" contains filenames and labels for the 2,790 images in the validation set used for the CHiMP Classifier network.</li> </ul> <p>In addition, an independent test set of images are included in the directory named "VMXI_Classification_Test_Dataset". Within this direcectory:</p> <ul> <li>The subdirectory named "VMXi_Classification_Test_Images" contains 1000 JPEG images with a resolution of 3376 × 2704 pixels.</li> <li>Each image is associated with a label describing the experimental outcome depicted in the images. The labels are Clear, Crystals, Precipitate or Other.</li> <li>The file "unambiguous_test_dataset.csv" contains filenames and labels for the 632 images in the test set where three experts independently agreed on a label.</li> <li>The file "mostly_unambiguous_test_dataset.csv" contains filenames and labels for the 949 images in the test set where at least two experts independently agreed on a label.</li> <li>The file "original_expert_labels.csv" contains filenames for all 1000 images and the labels given by three experts independently. The column headed "expert_1_1" refers to labels given by expert number 1 at a timepoint 6 months prior to categorising the images again, given in the column "expert_1_2". The columns "expert_2" and "expert_3" contain the labels given by experts 2 and 3 respectively.</li> <li>The <em>unambiguous</em> and <em>mostly unambiguous</em> test sets were created from the categories chosen by "expert_1_2", "expert_2" and "expert_3"</li> </ul>
Prioritising GitHub Priority Labels - Data Set and Software
<p>This is the data set and software produced for the paper <em>Prioritising GitHub Priority Labels</em>, J. Caddy and C. Treude.</p> <p>The CSV file contains a manually categorised set of GitHub issue labels that are priority-related. They have been ranked and normalised into three values; "High", "Medium", and "Low" priorities. These labels have been gathered from the 5000 most-starred repositories on GitHub as of 2022-06-01.</p> <p>The Python script makes use of this data set as an example, and will retrieve the highest priority issues from all of the repositories contributed to by the author specified.</p> <p>Run the python script from the same directory as the CSV file, providing the username you wish to see the highest priority issues for as the first command line argument. Supply your GitHub Personal Access Token either at the prompt so it's not displayed, or as the second command line argument.</p>
Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free Fiber-optic Raman Spectroscopy
<p>Dataset for the manuscript "<span>Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free </span><span>F</span><span>iber-optic Raman Spectroscopy"</span></p>
Fluorescent images of actin and DAPI-labelled MCF10A, MCF7 and MDA-MB-231 cell lines
<p>This dataset of cell images was generated to understand the morphological changes between less and more metastic cancer cells and between normal and cancerous cells. They have been used in the linked publications.</p>
Waikato Multi Label Aerial Imagery Dataset 2012
<p>This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken ~5 years prior to the referenced dataset. Below is the description of the referenced dataset</p> <p>Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.</p>
Waikato Multi Label Aerial Imagery Dataset
<p>Effective land use management is crucial for balancing development against environmental sustainability, preservation of biodiversity, and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks to better inform policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region, spanning 25,000 km\(^2\), specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with three supplementary sets. Each image in the main dataset is annotated with 33 fine-grained, multi-labeled classes and approximate segmentation masks of the classes, and the three supplementary datasets cover spatially coincident satellite imagery and aerial imagery five years prior and five years later from the main dataset. </p>
Waikato Multi Label Aerial Imagery Dataset 2023
<p>This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken ~5 years after the referenced dataset. Below is the description of the referenced dataset</p> <p>Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.</p>
Waikato Multi Labelled Satellite Imagery Dataset
<p>This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken with using Sentinel-2 at 10m spatial resolution. Below is the description of the referenced dataset</p> <p>Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.</p>
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and other sources about the 2024 outbreak of Measles
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: <a href="https://doi.org/10.48550/arXiv.2406.07693" rel="nofollow">https://doi.org/10.48550/arXiv.2406.07693</a>)</p> <p><strong>Abstract</strong></p> <p>This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.</p>
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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.