Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,612
datasets available to search
ShareScore release 0.9.0
Dataset results
4,612 results for “labelled”
The yin and yang of hsa-miR-1244 expression levels during activation of the UPR control cell fate - Raw data for label-free holographic microscopy
<p>Regulation of endoplasmic reticulum (ER) homeostasis plays a critical role in maintaining cell survival. When ER stress occurs, a network of three pathways called the unfolded protein response (UPR) is activated to reestablish homeostasis. While it is known that there is cross-talk between these pathways, how this complex network is regulated is not entirely clear. Using human cancer and non-cancer cell lines, two different genome-wide approaches, and two different ER stress models, we searched for miRNAs that were decreased during the UPR and surprisingly found only one, miR-1244, that was found in all these conditions. The activation of both UPR adaptive and apoptotic signaling pathway was confirmed by parallel genome-wide mRNA expression arrays for Calu 3 cells and next generation sequencing for 16HBE14o- cells. We also verified that ER-stress related downregulation of miR-1244 expression occurred with 5 different ER stressors and was confirmed in another human cell line (HeLa S3). These analyses demonstrated that the outcome of this reduction during ER stress supported both IRE1 signaling and elevated BIP expression. Further analysis also revealed that this novel miRNA impacted all three pathways of the UPR using inhibitors specific for IRE1, ATF6, and PERK. This is the first example of a complex mechanism by which this miRNA serves as a regulatory check point for all 3 pathways that is switched off after UPR activation. In summary, the results indicate that ER stress reduction of <em>miR-1244 </em>expression contributes to the pro-survival arm of UPR.</p> <p>For real-time monitoring of cell viability, we applied real-time and label-free holographic microscopy-based monitoring of cell death and viability using HoloMonitor M4<strong>®</strong> time-lapse cytometer (Phase Holographic Imaging PHI AB, Lund, Sweden). Holographic microscopy was used to follow the optical thickness and irregularity of cells exposed for up to 24 h to Tm or Tg in the presence or absence of <em>miR-1244 </em>mimic or antagomiR. The images from up to 8 independent optical fields were collected and analyzed according to manufacture instructions with HoloMonitor® App Suite software. Healthy cells are irregular in shape and thin, whereas dying cells are round and thick . For all analysis, the same cells parameters qualification was applied.</p> <p> </p> <p> </p> <p><strong>Figure 4. Overexpression of </strong><strong>miR-1244 promotes cell death. </strong>The results of real-time monitoring of cell viability are shown with the real time and label free holographic microscopy using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later monitored up to 24 h. Images were collected every 15 min (from 8 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 5. </strong><strong>miR-1244 influences the fate of cells challenged with Tm induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tm (2.5 µg/ml) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. <a name="OLE_LINK3"></a>Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 6. Exogenous </strong><strong>miR-1244 influences the fate of cells challenged with Tg induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tg (500 nM) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p> </p> <p> </p>
BDFoodSent: A Large-Scale Sentiment-Labeled Restaurant Review Dataset from Bangladesh
<p>BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.</p> <p> </p> <h3>Dataset Statistics</h3> <p>Total Reviews: 334,119</p> <p>Features/Columns: 19</p> <p> </p> <h3>Potential Applications</h3> <p>Sentiment Analysis</p> <p>Restaurant Review Classification</p> <p>Customer Satisfaction Analysis</p> <p>Opinion Mining</p> <p>Natural Language Processing Research</p> <p>Food Service Industry Analysis</p>
Label-free multiplexed detection of diabetic retinopathy biomarkers using fiber optic biosensors: towards lab-in-the-tear
<p>Raw experimental data on label-free detection of diabetic retinopathy biomarkers using fiber optic biosensors. This data contains information on the multiplexed and separate detection of LCN1 and VEGF diabetic retinopathy biomarkers in artificial tears. </p>
Training Data Label Distributions
<p>A comparison of the label distribution of the full training dataset of OGB-PPA and the 80% and 80-99% of the dataset sorted by graph size. </p>
Dataset for "In silico-labeled ghost cytometry"
<p>Data and code for the analyses in Ugawa <em>et al.</em> "In silico<em>-</em>labeled ghost cytometry" eLife.</p>
Dataset of ""Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution"
<p>Dataset of <em>Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution</em> by Sara Arganda, Ignacio Arganda-Carreras, Darcy G. Gordon, Andrew P. Hoadley, Alfonso Pérez-Escudero, Martin Giurfa and James F. A. Traniello.</p> <p>In this dataset, we are presenting:</p> <ul> <li>10 confocal brain images from <em>Pheidole spadonia </em>minors (in the original confocal TIFF format and in the open NRRD format), with manually segmented labels of 8 subregions (Optic Lobes, OL; Antennal Lobes, AL; Mushroom Body Medial Calyx, MB-MC; Mushroom Body Lateral Calyx, MB-LC; Mushroom Body Peduncle, MB-P; Central Complex, CX; Subesophageal zone, SEZ; and Rest of Central Brain, ROCB – in NRRD format) from one expert annotator.</li> <li>12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from one expert annotator.</li> <li>5 confocal brain images from <em>Pheidole spadonia </em>minors (“test brains”), with five sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from three expert annotators (one set from annotator 1, one set from annotator 2 and three sets from annotator 3, to evaluate inter and intra person differences).</li> <li>1 group-wise template generated from the 10 confocal brain images from <em>Pheidole spadonia </em>minors, with three sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>5 group-wise templates generated from the 9 confocal brain images from <em>Pheidole spadonia </em>minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>1 group-wise template generated from 12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>7 sets of automatic labels for the 5 “test brains”: 3 sets of “Direct Labels”, 3 sets of “Consensus Labels”, 1 set of “Multispecies Template Labels”.</li> </ul> <p>Brain of minor workers were dissected from the ant head capsule in ice cold HEPES-buffered saline and were fixed and immunohistochemically stained using SYNORF1 (a monoclonal <em>Drosophila</em> synapsin I antibody obtained from the Developmental Studies Hybridoma Bank, catalog 3C11) and secondarily stained using Alexa Fluor 488 for visualization of neuropil (slightly modified from Ott, 2008). Later, brains were mounted in methyl salicylate and imaged on an Olympus Fluoview BX50 laser scanning confocal microscope with a ×20 objective at a resolution of ~0.7 × 0.7 × 5µm/voxel. All brain tissue manipulation, staining and recording was performed by Darcy G. Gordon. Brain images were obtained in TIFF format by the confocal microscope and then opened and saved as Amira Mesh (.am) stack images in Amira (version 6.0). Manual segmentation of each brain was done using Amira (version 6.0 or 2019.2). Labels were traced on eight compartments in only one brain hemisphere, except for the CX, SEZ and ROCB, which lack a clear subdivision between hemispheres. Brain grey image stacks and labels were transformed to NRRD format for template construction using the Fiji plugin SaveAsGzipNrrd<a href="#_ftn1">[1]</a>. Volume and volume similarity of labels were calculated using the Fiji toolbox MorphoLibJ<a href="#_ftn2">[2]</a>.</p> <p><strong>Acknowledgements: </strong>We thank Ming Huang (from Dr. Diana Wheeler’s laboratory) who kindly provided access to colonies from four species of the hyperdiverse ant genus <em>Pheidole</em> (<em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana </em>and <em>P. obtusospinosa</em>). This research was supported by National Science Foundation grants IOS 1354291 and IOS 1953393 to JT, a Marie Skłodowska-Curie Individual Fellowship BrainiAnts-660976 and Ayudas destinadas a la atracción de talento investigador a la Comunidad de Madrid en centros de I+D. This work is supported in part by the University of the <a href="https://www.sciencedirect.com/topics/engineering/basque-country">Basque Country</a> UPV/EHU grant GIU19/027.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> https://github.com/iarganda/tefor</p> <p><a href="#_ftnref2">[2]</a> https://imagej.net/plugins/morpholibj</p>
Label-based expectations affect reward perception in bumblebees
<p>While classic models of animal decision-making assume that individuals objectively assess the absolute value of options, decades of research have shown that rewards are often evaluated relative to prior experience, creating 'contrast effects'. Contrast effects are often assumed to be purely sensory, yet consumer psychology tells us that label-based expectations can affect value perception. However, this has rarely been tested in non-model systems. Bumblebees forage on a variety of flower types that vary in their signals and rewards and show clear contrast effects when rewards are lower than their immediate previous experience. Here we manipulated bees' expectations of a stimulus' quality, before downshifting the reward to induce incentive contrast. We found that contrast effects were not solely driven by prior experience with a better reward, but also influenced by experience with associated stimuli. Bees were faster to accept the lower-quality reward when it was paired with a novel rather than a familiar stimulus. We then explored the boundaries of these label-based expectations by testing bees along a stimulus gradient and found that expectations generalized to similar stimuli. Such reference-dependent evaluations may play an important role in bees' foraging choices, with the potential to impact floral evolution and plant community dynamics.</p>
Paddy Rice Labeling Sites in South Korea (2018)
<p>The paddy rice was visually interpreted at 30 sites in South Korea. The sites were selected at each province by a proportional stratified sampling method according to the paddy rice area statistics(Statistics Korea), so the dataset can be used for the validation on model generalization over the entire country. The paddy rice areas were visually interpreted by using Google Earth Pro and street view services(<a href="https://map.naver.com">https://map.naver.com</a>, <a href="https://map.kakao.com">https://map.kakao.com</a>) and updated to the state of 2018.</p> <p>Supported by the CALLISTO (No. 101004152) project, which has been funded by EU Horizon 2020 programs.</p>
A formative usability study of workflow management systems in label-free digital pathology - Data and Code
<p>This repository holds the necessary data and code as well as a descriptive Readme file that was used for our publication "A formative usability study of workflow management systems in label-free digital pathology" by Markus Jelonek et al. (2022), submitted to F1000Research.</p> <p> </p> <p>Abstract:</p> <p>We present a formative usability study that investigates the usability of different<br> workflow management systems in the field of biomedical data analysis. Specifically, we study a task in the field of so-called label-free digital pathology and investigate one graphical user interface based workflow and one script-based workflow to solve the task. Our main intention is to gain first insights into the systematic study of usability in the context of biomedical image analysis, and formulate experiences and guidelines for future usability studies dealing with workflow management systems. Embedded in a specific setup dealing with label-free digital pathology, the core question behind our contribution is how usability studies for scientific workflow management can be conducted, and how they can be used systematically to improve such tools. Further, we address specific questions about the resource utilisation and management of usability studies, including the recruitment of participants as well as the design of specific workflows to be investigated.</p>
A formative usability study of workflow management systems in label-free digital pathology - Questionnaires
<p>This repository holds the necessary questionnaires, participant data, interview questions and data, as well as a descriptive Readme file that was used for our publication "A formative usability study of workflow management systems in label-free digital pathology" by Markus Jelonek et al. (2022), submitted to F1000Research.</p> <p> </p> <p>Abstract:</p> <p>We present a formative usability study that investigates the usability of different<br> workflow management systems in the field of biomedical data analysis. Specifically, we study a task in the field of so-called label-free digital pathology and investigate one graphical user interface based workflow and one script-based workflow to solve the task. Our main intention is to gain first insights into the systematic study of usability in the context of biomedical image analysis, and formulate experiences and guidelines for future usability studies dealing with workflow management systems. Embedded in a specific setup dealing with label-free digital pathology, the core question behind our contribution is how usability studies for scientific workflow management can be conducted, and how they can be used systematically to improve such tools. Further, we address specific questions about the resource utilisation and management of usability studies, including the recruitment of participants as well as the design of specific workflows to be investigated.</p>
An evidence-based study on issue labeling in Github-based repositories (Supplementary material)
<p>The open-source software community has grown in size and importance over the years. As a consequence, the number of project contributors has increased considerably. The capability of open-source project repositories to accommodate issue reports is essential. An issue report encompasses a large set of data that describes the necessary changes a software should handle. As developers need detailed information to reproduce and find them, incomplete information is a severe problem that may influence triage and defect detection leading to delays in project maintenance. Issue trackers commonly use the labeling method to add extra details to issues. Knowing the importance of labels, this dissertation focus on investigating the usage, creation, and similarities in the context of the issue lifecycle in both maintenance and evolution in the repository issue trackers of the largest and most popular code hosting platform, Github. In addition, it analyzes the number of labeled and unlabeled issues in the repository and the connection between the issues' components, with an analysis focused on the lifecycle. The results indicate a significant correlation between repositories with many issues and the creation of labels, but not all repositories use them. 64.58% of the repositories insert new labels as the project evolves. 73.14% repositories applied on issues the Github standard labels. We also found an influence of primary issue fields such as title, description, and comments in most issue labels, impacting the creation and labeling issues. These numbers show that issue labeling is of prominent relevance for project maintenance and evolution. It provides developers with an easy and convenient way to inform about an incoming issue reported by systems users.</p>
Labeled Images of Sand and Coins v2
<p>179 images of sand and coins, and corresponding label image masks and associated files. For the purposes of training a model for image segmentation.</p> <p>Contents:</p> <p>1. images.zip</p> <p>2. masks.zip</p> <p>3. rotimages4makesense.py</p> <p> </p> <p>images.zip contains a folder named 'images', itself containing 179 images. Imagery exif metadata have been modified to remove any 'auto-rotation' parameters, using the provided script `rotimages4makesense.py`</p> <p>masks.zip contains a folder named 'masks', itself containing 179 label images. Label images are white (value=255) where there is a coin, otherwise black (value=0)</p> <p>All images are 8-bit JPEG format and were acquired by the author. All label images were made by the author using the online program makesense.ai, downloading in VGG JSON format, then rasterizing the contents.</p> <p> </p>
Even when you know it is a placebo, you experience less sadness: First evidence from an experimental open-label placebo investigation (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Even when you know it is a placebo, you experience less sadness: First evidence from an experimental open-label placebo investigation. <em>Journal of Affective Disorders</em>. </strong></p> <p><em>Background:</em> Recent studies demonstrate substantial effects of deceptive placebo on experimentally induced sadness,<br> even on autonomic activity. Whether deception is necessary, remains to be elucidated. We investigated the<br> effect of an open-label placebo (OLP) treatment, i.e. an openly administered placebo delivered with a convincing<br> rationale for its sadness-protecting effect.<br> <em>Methods:</em> Eighty-four healthy females were randomized to an OLP group or a no-treatment control group. All<br> participants received the same detailed information about the OLP effect, only the OLP group received an OLP<br> nasal spray. Before and after the OLP intervention, participants underwent a sad mood induction procedure<br> combining self-deprecating statements (Velten's method) and sad music. Sadness was assessed by the Positive<br> and Negative Affect Schedule (PANAS-X). Autonomic activity was measured continuously.<br> <em>Results:</em> Participants in the OLP group reported a significantly attenuated increase in sadness upon mood induction<br> and less sadness after induction compared to the control group (d = 0.79). Regardless of intervention,<br> heart rate decreased during mood inductions with a more pronounced deceleration in the second mood induction.<br> <em>Limitations:</em> Generalizability is limited due to the selective sample and the reliance on an experimentally controlled<br> mood induction.<br> <em>Conclusion:</em> OLP treatment had a beneficial effect on perceived sadness, at least at the subjective level. Hence,<br> deception may not necessarily be required for placebos to modulate experienced sad mood. Investigating the<br> beneficial effects of OLP in (sub-)clinical samples would seem a promising and required next step towards a<br> clinical use of placebo-associated positive treatment expectations.</p>
Labeled data for citation field extraction
<p>Citations are an important part of scientific papers, and the proper handling of them is indispensable for the science of science. Citation field extraction is the task of parsing citations: given a citation string, extract authors, title, venue, doi etc. Since the number of citations is counted by hundreds millions, efficient computer based methods for this task are very important.</p> <p>The development of machine learning methods for citation field extraction requires ground truth: a large corpus of labeled citations. This dataset provides a very large (41M) corpus of labeled data obtained by the reverse process: we took structured citation lists and used BibTeX to generate labeled citation strings.</p>
WHUL8-CDb, a block-level labelled cloud detection dataset collected from Landsat 8
<p>WHUL8-CDb was collected for the training of weakly-supervised cloud detection methods. It includes 500 globally distributed Landsat 8 Level-1C images (Fig. 1) collected following the distribution of the L8 Biome dataset as it comes to biomes and dominant land cover types. The motivation is that clouds are similar with each other in different regions, while the backgrounds are very different. In order to increase the diversity of training samples, we selected images with more background pixels by setting the cloud percentage from 0 to 50 when searching images. To handle all cloud situations, 5 images full of clouds were collected and included in WHUL8-CDb.</p> <p>The WHUL8-CDb dataset canbe downloaded on the following links:</p> <p>First part (1~11) : <a href="http://doi.org/10.5281/zenodo.6420035">https://doi.org/10.5281/zenodo.6420035</a></p> <p>Second part (12~22) : <a href="http://doi.org/10.5281/zenodo.6467663">https://doi.org/10.5281/zenodo.6467663</a></p> <p>Third part (23~33): <a href="http://doi.org/10.5281/zenodo.6510533">https://doi.org/10.5281/zenodo.6510533</a></p> <p>Fourth part (34~44): <a href="http://doi.org/10.5281/zenodo.6510679">https://doi.org/10.5281/zenodo.6510679</a></p> <p>Fifth part (45~55): <a href="http://doi.org/10.5281/zenodo.6510681">https://doi.org/10.5281/zenodo.6510681</a></p> <p>Sixth part (56~65): <a href="http://doi.org/10.5281/zenodo.6510681">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.6510683">10.5281/zenodo.6510683</a></p>
Echolocation clicks and anthropogenic detections with neural network labels in Hawaiian Island HARP data from Kona, Kaua`i, and Pearl and Hermes Reef
<p><span>This dataset consists of echolocation clicks and detections of anthropogenic signals at three sites in the Hawaiian Islands Archipelago. These sites are </span><span>Hawaii/Hawaii_K, </span><span>Kauai/KA, and </span><span>Pearl and Hermes Reef/PHR. </span><span>Echolocation clicks were grouped into 5 minute bins, for which summary data is provided. Files are in .mat format that can be read using any desired coding language using a netcdf reading script. Files are separated by site, deployment, and neural network class (i.e. sitedeployment_cbins_class or site_deployment_cbins_class). Manual labels are provided.</span></p>
OSM buildings noisy labels dataset
<p>This dataset contains tile imagery from the OpenStreetMap project alongside label masks for buildings from OpenStreetMap. Besides the original clean label set, additional noisy label sets for random noise, removed and added buildings are provided.</p> <p>The purpose of this dataset is to provide training data for analysing the impact of noisy labels on the performance of models for semantic segmentation in Earth observation.</p> <p>The code for downloading and creating the datasets as well as for performing some preliminary analyses is also provided, however it is necessary to have access to a tile server where OpenStreetMap tiles can be downloaded in sufficient amounts.</p> <p>To reproduce the dataset and perform analysis on it, do the following:</p> <ul> <li>unzip data.zip and code.zip</li> <li>create the folder structure from data</li> <li>Build and activate a python environment from environment.yml</li> <li>Insert the url of a suitable tile server for OSM tiles in line 76 of utils.py</li> <li>Execute download_OSM_dataset.py to download OSM image tiles alongside OSM labels</li> <li>Execute create_noisy_labels.py for the OSM dataset to create noisy label sets</li> <li>Divide the images and labels into train and test data. split_data.py can be used as a baseline for this, but pathnames have to be adjusted and the corresponding directories have to be created first.</li> <li>Call train_model.py to train a model on the data. Specify the data size and the label set by giving command line arguments as shown in train_model.sh</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Genetic monitoring on the world's first MSC eco-labeled common octopus (O. vulgaris) fishery in western Asturias, Spain
<p><strong>Allele frequencies file containing:</strong></p> <ul> <li>15 populations</li> <li>13 microsatellite markers</li> </ul> <p><em><strong>Populations:</strong></em></p> <ul> <li>21PS: Pasaia (Basque country, Spain). Fishery season (FS): 2020-21</li> <li>18RB: Ribadesella (Asturias, Spain). FS: 2017-18</li> <li>21RB: Ribadesella (Asturias, Spain). FS: 2020-21</li> <li>18CU: Cudillero (Asturias, Spain). FS: 2017-18</li> <li>21CU: Cudillero (Asturias, Spain). FS: 2020-21</li> <li>07PV: Puerto de Vega (Asturias, Spain). FS: 2006-07</li> <li>18PV: Puerto de Vega (Asturias, Spain). FS: 2017-18</li> <li>21PV: Puerto de Vega (Asturias, Spain). FS: 2020-21</li> <li>18TP: Tapia de Casariego (Asturias, Spain). FS: 2017-18</li> <li>21TP: Tapia de Casariego (Asturias, Spain). FS: 2020-21</li> <li>21BU: Bueu (Galicia, Spain). FS: 2020-21</li> <li>07OL: Olhão (Algarve, Portugal). FS: 2006-07</li> <li>21OL: Olhão (Algarve, Portugal). FS: 2020-21</li> <li>21SA: San Andrés (Canary Islands, Spain). FS: 2020-21</li> <li>21BC: Barcelona (Catalonia, Spain). FS: 2020-21</li> </ul> <p><em><strong>Microsatellite markers (GenBank accession number):</strong></em></p> <p>OCT08 (AF197132); VULG15 (LC003035); VULG14 (LC003034); VULG07 (LC003028); OVUL10 (JN579699); VULG12 (LC003032); VULG13 (LC003033); VULG06 (LC003027); OVUL09 (JN579698); VULG04 (LC003026); OVUL08 (JN579697); OV10 (AF197134); VULG10 (LC003030).</p>
giaIndoorLoc – Auto-labeled WLAN + IMU dataset generated via VI-SLAM2tag
<p>This repository holds the data that belongs to the publication:</p> <p>M. Laska, T. Schulz, J. Grottke, C. Blut and J. Blankenbach, "VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization", [arXiv:2207.02668]</p> <p>which is to appear at the 2022 IPIN conference. </p> <p>It is split into the following sub-parts:<br> - giaIndoorLoc_raw: Raw data recorded via the VI-SLAM2tag android app (https://github.com/laskama/VI-SLAM2tag_app)<br> - giaIndoorLoc: Annotated dataset (generated from giaIndoorLoc_raw)<br> - evaluation_data: Raw trajectory data that is used during evaluation of labeling accuracy of VI-SLAM2tag (Control-Point + Total Station (Tachymeter))<br> - model_evaluation: Model weights of fitted models used during baseline performance section (VII-B) of paper. Required for reproducing experiments with repo (https://github.com/laskama/mCELindoorLoc)</p> <p> </p> <p>For a detailed description, please refer to the given paper and the additional github repositories that host the implementations:</p> <p>- https://github.com/laskama/VI-SLAM2tag_post</p> <p>- https://github.com/laskama/VI-SLAM2tag_app</p> <p>- https://github.com/laskama/mCELindoorLoc</p>
Volumetric imaging of fluorescently labeled BPAE cells
<p>Using the Nanoimager-S microscope (ONI, Oxford Nanoimaging) with a sCMOS sensor (Hamamatsu, ORCA-Flash4.0 V2), FluoCells™ Prepared Slide #1 (Thermo, #F36924) were imaged with a 100X, 1.4 NA, oil-immersion objective (Olympus). DAPI, Alexa-488 and MitoTracker™ Red excitation was delivered by 405 nm, 473 nm and 561 nm lasers, respectively. Light was collected while using two emission filters (1: 525/50; 2: Band 1 575-616.5) and a Channel Splitter dichroic 561 LP. A 3D Z-stack of the sample was acquired for each channel. 22 frames were generated, each separated 50 nm from each other in Z.</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.