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4,612 results for “labelled”
Mummy label
ID: MKGHamburg 1934.2.a <br> Object type: Mummy label <br> Origin: Egypt <br> Date/Period: 1.–3. cent AD <br> Material: Wood <br> Measurements: 8.4 cm (height); 21.7 (width); 1.0 cm (depth) <br> Language: Greek <br> Museum/Collection: Museum für Kunst und Gewerbe Hamburg Inv. 1934.2.a <br> The research for this 3D object was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2176 'Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures', project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg. Source: Objaverse 1.0 / Sketchfab
Mummy label
ID: MKGHamburg 1934.2.c <br> Object type: Mummy label <br> Origin: Egypt <br> Date/Period: Hellenistic/Roman <br> Material: wood <br> Measurements: 3.5 cm (height); 15 cm (width); 0.7 cm (depth) <br> Language: Greek / Demotic <br> Museum/Collection: Museum für Kunst und Gewerbe Hamburg Inv. 1934.2.c <br> The research for this 3D object was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2176 'Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures', project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg. Source: Objaverse 1.0 / Sketchfab
MiL-FISH: Multi-labelled oligonucleotides for fluorescence in situ hybridisation improve visualization of bacterial cells
<p>Comparison of mono-, MiL- and CARD-FISH on LR-White embedded <em>Olavius algarvensis</em> cross sections A: Ethanol preserved specimen, cu = cuticle, sym = symbionts, sep = septum, epi = epidermis, mu = muscle, vbv = ventral blood vessel & nerve chord, chl = chloragogen tissue, grid square = example of region shown in images B, C, D and E.</p> <p>Probes for Gammaproteobacteria (Gam42a; green) and a subgroup of sulfate-reducing Deltaproteobacteria (DSS658; red) on B) mono-FISH on ethanol preserved specimen, 19 hour hybridisation. C) mono-FISH on ethanol preserved specimen, 3 hour hybridisation and D) CARD-FISH on Carnoy’s / PFA fixed specimen, 3-hour hybridisation. E) MiL-FISH on Carnoy’s / PFA fixed specimen, 3 hour hybridisation. Scale bar = 5 µm.</p>
MiL-FISH: Multi-labelled oligonucleotides for fluorescence in situ hybridisation improve visualization of bacterial cells
<p>Sections of LR-White embedded <em>Olavius algarvensis </em>eggs. A) DAPI stained overview of egg after first cleavage, grid square = region in B, cle = cleavage. B) Gamma- (ii) and Delta- (i) proteobacteria hybridised with 4x labelled Gam42a & 4x labelled DSS658 probe respectively. Autofluorescence of egg yolk is overcome and bacteria are seen to closely associated with the developing embryo. y = egg yolk C) DAPI stained overview of juvenile worm in egg, grid square = region in D. D) Gamma 1 symbiont phylotype (iii) hybridised with 16S rRNA specific probe labelled with 2x FITC and 2x Cy3 to produce yellow in the overlay. Symbiont cells are incorporated between cuticle and epidermis and in close proximity to egg yolk. y = egg yolk, c = cuticle. Scale: A & C = 50 µm, B & D = 5 µm.</p>
MiL-FISH: Multi-labelled oligonucleotides for fluorescence in situ hybridisation improve visualization of bacterial cells
<p>Epifluorescence images of MiL-FISH labelled microorganisms: A) <em>Beggiatoa sp. </em>hybridised with Gam42a, B) <em>Desulfococcus biacutus </em>with DSS658, C) <em>Roseobacter sp. </em>with Ros537, D) <em>Sulfurimonas denitrificans </em>with EPSY914, E) <em>Rhodopirellula sp. </em>SH1 T with PLA46, F) <em>Gramella forsetii </em>with CF319a, G) <em>Metallosphaera sedula </em>with Arch915, H) Composite image of all seven microbial partners in an artificial mix. Letters correlate to images of individual organisms A-G. Scale bar: A & H = 10 µm, B-G = 5 µm.</p> <p>From:</p> <p>Schimak MP, Kleiner M, Wetzel S, Liebeke M, Dubilier N, Fuchs BM. 2015. MiL-FISH: Multi-labelled oligonucleotides for fluorescence in situ hybridisation improve visualization of bacterial cells. Applied and Environmental Microbiology. Accepted manuscript posted online. doi:10.1128/AEM.02776-15</p>
EEG Data for: "Learning from Label Proportions in Brain-Computer Interfaces"
<p>Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. </p> <p>The dataset is used and described in the following journal article:</p> <p><em>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.</em></p> <p><strong>Please cite the above article when using the data.</strong></p> <p>The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.</p> <p>In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:</p> <p><em>Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.</em></p> <p>The following files are available:</p> <p>description.pdf: <strong>Full description of the dataset</strong><br> offline_auditory.zip: <strong>Data from the auditory offline study with N=5 subjects</strong><br> offline_visual.zip: <strong>Data from the visual offline study with N=5 subjects</strong><br> online_study_1-7.zip: <strong>Data from the online study for subjects 1-7</strong><br> online_study_8-13.zip: <strong>Data from the online study for subjects 8-13</strong><br> sequence.mat: <strong>Sequence data necessary for applying LLP to the online study. It is the same for all subjects</strong></p> <p>We will create a git repository with example code soon.</p>
Contrasting effects of indigenous arbuscular mycorrhizal fungi on nitrogen absorption of C3 and C4 grasses: Evidence from microcosm and 15N labeling experiments
<p><strong><em>Background and aims</em></strong></p> <p>Nitrogen (N) captured by arbuscular mycorrhizal (AM) symbiosis is a major pathway in the N uptake of host plants. However, the relative contribution of arbuscular mycorrhizal fungi (AMF) to N uptake in different plant functional types has not been well assessed.</p> <p><strong><em>Methods</em></strong></p> <p>Two dominant plant species in semiarid steppe ecosystems on the Mongolian plateau, i.e. <em>Leymus chinensis </em>(C<sub>3</sub> grass) and <em>Cleistogenes squarrosa</em> (C<sub>4</sub> grass), were selected in this study. We conducted a greenhouse manipulation experiment using novel microcosms combined with <sup>15</sup>N labeling techniques and investigated the effect of indigenous AMF on plant growth and quantified their relative contribution to N uptake under high and low levels of available soil N. </p> <p><strong><em>Results</em></strong></p> <p>Indigenous AMF contribute more to N uptake in C<sub>3</sub> grass than that in C<sub>4</sub> grass, and mycorrhizal partners act as parasites for C<sub>4</sub> plant growth. For <em>L. chinensis</em>, indigenous AM symbiosis suppressed plant growth under low soil N but improved plant growth under high soil N conditions. AMF contributed to <em>c.</em> 23% and 20% of the total plant N uptake under low and high soil N conditions, respectively. For <em>C. squarrosa</em>, indigenous AM symbiosis consistently inhibited plant growth under both low and high soil N conditions, and the percent contributions of AMF to N uptake were only <em>c.</em> 9% and 7%, respectively.</p> <p><strong><em>Conclusions</em></strong></p> <p>Our results demonstrate that indigenous AM symbiosis plays a vital role in N uptake by host plants, even in the absence of a positive growth response. AMF can modify the fitness of C<sub>3</sub> and C<sub>4</sub> grasses and thereby alter plant community composition and ecosystem N cycling, particularly under high N conditions. Our study has important implications for improving global N cycling models in the face of increasing global N deposition.</p>
SEM Images of Arrays of III-V Nanowires with Labelled Data
<p>The Dataset is a .zip folder.</p><p>The Dataset contains 240 .tiff images in black and white, each paired by file name to a .json file containing labeling information. </p><p>Each image consists of an array of 66 nanowire structures. Some images contain parasitic crystals and defective wires. Wires growing from seed surfaces parallel to and misaligned with the array walls are present.</p><p>The .json label files were created and can be processed with LabelMe (<a href="https://github.com/wkentaro/labelme">https://github.com/wkentaro/labelme</a>). The explanation for the labels can be found in the associated open-access paper currently submitted for publication.</p><p>The Licensor, IBM Corp.</p>
Label-free imaging of DNA interactions with 2D materials
<p>Raw images and data analysis related to the manuscript entitled "Label-free imaging of DNA interactions with 2D materials"</p>
Labelled dataset to classify direct deforestation drivers in Cameroon
<p><strong>Overview</strong></p> <p>This dataset includes the images (visible bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon. </p> <p><strong>Description of the files</strong></p> <ul> <li>'my_examples_landsat_final_detailed.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes: ‘Oil palm plantation’, ‘Timber plantation’, ‘Fruit plantation (e.g. banana)’, ‘Rubber plantation’, ‘Other large-scale plantation (e.g. tea, sugarcane)’, ‘Grassland/Shrubland’, ‘Small-scale oil palm plantation’, ‘Small-scale maize plantation’, ‘Other small-scale agriculture’, ‘Mining’, ‘Selective logging’, ‘Infrastructure’, ‘Wildfire’, ‘Hunting’, ‘Other’)</li> <li>'my_examples_planet_final_detailed.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes)</li> <li>'my_examples_landsat_final.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes: 'Plantation', 'Grassland/Shrubland', 'Smallholder agriculture', 'Other')</li> <li>'my_examples_planet_final.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes)</li> <li>'my_examples_landsat_detailed_timeseries.zip': Landsat-8 images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis </li> <li>'my_examples_planet_detailed_timeseries.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis</li> <li> <p>‘labels.zip’: in csv files, the labels for each image in each folder described above (image identified by folder and coordinates or ‘path’) and matches the format of the csv files used as inputs to train, validate and test our classification model</p> <p>For ‘labels.zip’, we have subfolders for Landsat and PlanetScope. Then, for each type of imagery, we have subfolders for ‘detailed’, ‘groups’ and ‘time series’ which correspond to the different ‘my_examples’ folders listed above. </p> <p>For each folder, subfolders named with the coordinates of the centre of the images contain each:<br>• A folder ‘images’, with a sub-folder ‘visible’ containing the PNG RGB image; and a sub-folder ‘infrared’ containing the infrared bands in a NPY file.<br>• A folder ‘auxiliary’ with topographic and forest gain information in a NPY format, OpenStreetMap and peat data in a JSON format, and a sub-folder ‘ncep’ containing all data from NCEP in a NPY format.<br>• The forest loss pickle file delimiting the area of forest loss.</p> </li> </ul> <p><strong>Details about the images</strong></p> <ul> <li> <p>For Landsat-8 data (courtesy of the U.S. Geological Survey), this dataset contains 332x 332 pixels RGB calibrated top-of-atmosphere (TOA) reflectance images pan-sharpened to a 15 m resolution (less than 20% cloud cover)</p> </li> <li> <p>For NICFI PlanetScope data (catalog owner: Planet), this dataset contains 332x 332 pixels monthly RGB composite with a 4.77 m resolution</p> </li> </ul> <p><strong>Details about the auxiliary data</strong></p> <ul> <li>Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine</li> <li>Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images</li> <li> From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter</li> <li>Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API</li> <li>Altitude in m, slope and aspect in 0.01° from Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine</li> <li>Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website</li> </ul> <p><strong>Details about Global Forest Change</strong></p> <p>For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution. </p> <p><strong>License</strong></p> <p>The NICFI PlanetScope images fall under the same license as the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> (data in 'my_examples_planet_final.zip', 'my_examples_planet_final_detailed.zip', 'my_examples_planet_detailed_timeseries.zip': subfolders '[coordinates]'>'images'>'visible'). </p> <p><a href="https://wiki.osmfoundation.org/wiki/Licence/Attribution_Guidelines">OpenStreetMap®</a> is open data, licensed under the <a href="https://opendatacommons.org/licenses/odbl/1-0/">Open Data Commons Open Database License (ODbL)</a> by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0)</a>.</p> <p>The rest of the data is under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. The data has been transformed following the code that can be found via this link: <a href="https://github.com/aedebus/Cam-ForestNet">https://github.com/aedebus/Cam-ForestNet</a> (in 'prepare_files').</p> <p> </p>
Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled scRNAseq
<p><span>During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities.<span> Here, we </span>combine<span> </span>single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We then introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell-types. Our study provides a quantitative approach to study mRNA regulation during</span> a dynamic spatio-temporal response<span>.</span></p> <p> </p> <p>This repository contains the raw microscopy data that is analyzed in Figures 6F-I and Supplementary Figure S4 B-D.</p>
Emotion4MIDI: A Lyrics-Based Emotion-Labeled Symbolic Music Dataset
<p>This dataset includes emotion labels for the publicly available MIDI dataset, namely Lakh MIDI Dataset and Reddit MIDI dataset. The values represent the probability of containing a particular emotion. For a single song, more than one emotion can be present, hence the values don't add up to 1.</p>
Examples for running TraceGroomer: format and normalise your labeled metabolomics data for DIMet analysis
<p>Examples to test our tool <a href="https://github.com/cbib/TraceGroomer">TraceGroomer</a>, to prepare your files for DIMet (Differential analysis of Isotope-labeled Metabolomics data).</p> <p>Each example represents one type of input supported by TraceGroomer. Please download the entire .zip and find inside the example that best suits your case.</p> <p>The new version v2 contains four types of input:</p> <ol> <li>IsoCor .tsv generated file: <em>example-isocor_data</em></li> <li>The rule of "three .tsv files" , i.e. sampleMetadata, variableMetadata and dataMatrix: <em>example-ruletsv_data</em></li> <li>custom or generic .xlsx file: <em>example-sheet_data</em></li> <li>VIB MEC .xlsx file: <em>example-vib_data</em></li> </ol> <p>Visit the <a href="https://github.com/cbib/TraceGroomer/wiki">TraceGroomer Wiki page</a> for further information and how to run the tool on the provided examples. </p> <p><strong>For users of the Galaxy </strong>version of Tracegroomer: please only use the 'data/' folder (ignore the 'groom_files/' folder)</p>
Simple labelled data for WildFi tag lifting experiment
<p>A tiny dataset with ~2000 recordings. We used it to test the WildFi's capabilities of using a classifier to derive simple certain states of the tag from its sensor readings.</p> <div></div>
Supplementary methods and data for: Dogs with a vocabulary of object label remember labels for at least two years
<p>Long-term memory of words has a crucial role in the developing abilities of young children to acquire language. In dogs, the ability to learn object labels is present in only a small group of uniquely Gifted Word Learner (GWL) dogs. The ability of these dogs to acquire large vocabularies consisting of hundreds of names of dog toys through naturally occurring interactions in human families presents them as a valid model for studying language-related cognitive mechanisms. As they are very rare, little is known about the mechanisms through which they acquire such large vocabularies. In the current study, we tested the ability of five GWL dogs to retrieve 12 labelled objects two years after the object-label mapping acquisition. The dogs proved to remember the labels of between 3-9 objects. The results shed light on the process by which GWL dogs acquire an exceptionally large vocabulary of object names. As memory plays a crucial role in language development, these dogs supply a unique opportunity to study label retention in a non-linguistic species.</p>
The fully executable procedure of the U-Net model combined with the Multi-textRG algorithm to achieve fine ice-water classification ---- another 332 scenes of data-fused SIC labels.
<p>This data source is related to the manuscript titled "Combining the U-Net model and a Multi-textRG algorithm for fine SAR ice-water classification", which will be submitted to the journal---The Cryosphere. </p> <ul> <li>The"ready-to-train-fused_01.zip" to "ready-to-train-fused_10.zip" includes 200 scenes of data-fused SIC labels accessible with doi: 10.5281/zenodo.10973107, https://zenodo.org/records/10973107. </li> <li>The "ready-to-train-fused_11.zip" to "ready-to-train-fused_21.zip" includes another 332 scenes of data-fused SIC labels. </li> </ul>
Data associated with 'Closing the stellar labels gap: Stellar label independent evidence for [α/M] information in Gaia BP/RP spectra'
<p>We present a stellar label independent model for Gaia BP/RP (XP) spectra, which does not rely on stellar labels to simulate XP spectra. Here, we provide all of the data required to reproduce <a href="https://github.com/AlexLaroche7/xp_vae">our results</a>. All of the data files should be placed in the data folder. Below we brieflty summarize the contents of each:</p> <p>APOGEE/GAIA XP DATA:</p> <ul> <li>xp_apogee_cat.h5: Contains all Gaia XP data and APOGEE stellar labels used in this work.</li> <li>xp_corrs.npz: Contains Gaia XP covariance matrices for a subset of the test data to compute reconstruction errors.</li> </ul> <p>INTERMEDIATE DATA PRODUCTS:</p> <p><em>(All files below can be reproduced with our codebase. We simply include them to speed up runtime for reproducing our results.)</em></p> <ul> <li>lb23_xp_est_labels.npy: <a href="https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.1494L/abstract">Leung & Bovy (2023)</a> XP coeffficient spectra reconstructions (stellar label dependent implementaton)</li> <li>lb23_xp_est_no_labels.npy: Leung & Bovy (2023) XP coeffficient spectra reconstructions (stellar label independent implementaton)</li> <li>xp_wavelength_space.npy: Observed Gaia XP spectra in wavelength space</li> <li>err_wavelength_space.npy: Observed Gaia XP uncertainties in wavelength space</li> <li>apogee_norm.npz: Means and standard deviations which are used to pre-process Gaia XP data before inputting into our model</li> <li>zhang_stellar_params_xmatch.npz: <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z/abstract">Zhang, Green, Rix (2023)</a> stellar parameter estimates for subset of XP spectra (to compute reconstructions)</li> <li>vae_wavelength_space.npy: Our model XP coefficient reconstructions in wavelength space</li> </ul> <p>If you have any questions please reach out via email: alex.laroche@mail.utoronto.ca</p>
Toxic Sentence Classification Dataset with labels of categories such as religion, mental health, race, sex, body image, disability, physical abuse, and politics
<p>The dataset has a collection of various toxic sentences belonging to different categories. It was collected from various sources. It indicates which category each sentence belongs to. The values of the category columns are binary 1 or 0 indicating whether the sentence belongs to that particular category or not. Each sentence belongs to only 1 category. </p> <p> </p> <p>Columns:<br>1.comment_text: Contains toxic sentences that are insensitive and offensive, focusing on various categories.<br>2.mental_health: Binary value 1 indicates that the sentence focuses on mental health.<br>3.Race:Binary value 1 indicates that the sentence is racist.<br>4.sex:Binary value 1 indicates that the sentence focuses on sexuality.<br>5.body_image:Binary value 1 indicates that the sentence focuses on body image.<br>6.disability:Binary value 1 indicates that the sentence focuses on physical disability and related issues.<br>7.religion:Binary value 1 indicates that the sentence can be triggering to people who are extremely religious.<br>8.physical_abuse:Binary value 1 indicates that the sentence focuses on physical abuse issues.<br>9.politics:Binary value 1 indicates that the sentence focuses on political issues.</p>
A labelled dataset of the loud calls of four vertebrates collected using passive acoustic monitoring in Malaysian Borneo
<p><em>Passive acoustic monitoring data collection</em></p> <p>We collected data using first generation Swift autonomous recording units (ARUs) (Koch et al. 2016) with a microphone sensitivity of −44 (+/−3) dB re 1 V/Pa. The microphone frequency response was not measured but is assumed to be flat (+/− 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 40 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of −/+ 0.9 V. We collected acoustic data from one primary conservation area in Sabah, Malaysia: Danum Valley Conservation Area (with 11 recording units from March to July 2018). Danum Valley covers an area of roughly 440 km², and is characterized by lowland dipterocarp forest. Unlike many tropical forest regions, this area is considered 'aseasonal' due to its lack of clearly differentiated wet and dry seasons (Walsh and Newbery 1999). In Danum Valley, the ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours. We set the units on a 750 m grid structure, and preliminary field tests indicate that with these recording settings the detection range of gibbon vocalizations is ~ 400 m.</p> <p> </p> <p><em>Acoustic data processing </em></p> <p>We randomly chose approximately 500 h of recordings from Danum Valley Conservation Area to use to create a training dataset. We used a band-limited energy detector (BLED) to identify potential sounds of interest in the gibbon frequency range. For the BLED detector, we convert the 2-hr recordings into a spectrogram using a 1,600-point (100 ms) Hamming window (3 dB bandwidth = 13 Hz) with 0% overlap and a 2,048-point DFT, with the "seewave" package (Sueur et al. 2008). We then filtered the spectrogram to focus on the desired frequency range, specifically 0.5–1.6 kHz for Northern grey gibbons. For each unique time window in the recording, we determined the total energy across frequency bins which gave a single value for every 100 ms interval. Utilizing the "quantile" function in base R, we established the threshold to delineate signal from noise. Preliminary tests with varied quantile values revealed that the 15th quantile led to optimized recall for our target signal. This approach resulted in 1,439 unique sound events. The sound events were then annotated by a single observer (DJC) using a custom-written function in R to visualize the spectrograms into the following categories: great argus pheasant (<em>Argusianus argus</em>) long and short calls (Clink et al. 2021), helmeted hornbills (<em>Rhinoplax vigil</em>), rhinoceros hornbills (<em>Buceros rhinoceros</em>), female gibbons (<em>Hylobates funereus</em>) and a catch-all “noise” category. </p> <p><em>Update Version 5 and later</em></p> <p>Includes labeled test clips from a second conservation area, Maliau Basin Conservation Area, Sabah, Malaysia recorded during August 2019. The ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours.</p> <p> </p> <p><strong>References</strong></p> <p>Clink, D. J., Groves, T., Ahmad, A. H., & Klinck, H. (2021). Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus. <em>PloS one</em>, <em>16</em>(2), e0246564.</p> <p>Koch, R., Raymond, M., Wrege, P., & Klinck, H. (2016). SWIFT: A small, low-cost acoustic recorder for terrestrial wildlife monitoring applications. In <em>North American Ornithological Conference</em> (p. 619). Washington, D.C.</p> <p>Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. <em>Bioacoustics</em>, <em>18</em>, 213–226.</p> <p>Walsh, R. P., & Newbery, D. M. (1999). The ecoclimatology of Danum, Sabah, in the context of the world’s rainforest regions, with particular reference to dry periods and their impact. <em>Philosophical transactions of the Royal Society of London. Series B, Biological sciences</em>, <em>354</em>(1391), 1869–83. https://doi.org/10.1098/rstb.1999.0528</p> <p>Webb, C. O., & Ali, S. (2002). Plants and vegetation of the Maliau Basin Conservation Area, Sabah, East Malaysia. <em>Final Report to Maliau Basin Management Committee</em>.</p> <p> </p>
Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"
<p>Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"</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.