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FOP nutrient labels in Argentina: purchase intent, perception of healthiness and understanding in three processed food categories.
<p>Data analysis (non-statistically significant results) and raw survey response data.</p>
Portuguese Comparative Sentences: A Collection of Labeled Sentences on Twitter and Buscapé
<p>More and more customers demand online reviews of products and comments on the Web to make decisions about buying a product over another. In this context, sentiment analysis techniques constitute the traditional way to summarize a user’s opinions that criticizes or highlights the positive aspects of a product. Sentiment analysis of reviews usually relies on extracting positive and negative aspects of products, neglecting comparative opinions. Such opinions do not directly express a positive or negative view but contrast aspects of products from different competitors. </p> <p>Here, we present the first effort to study comparative opinions in Portuguese, creating two new Portuguese datasets with comparative sentences marked by three humans. This repository consists of three important files: (1) lexicon that contains words frequently used to make a comparison in Portuguese; (2) Twitter dataset with labeled comparative sentences; and (3) Buscapé dataset with labeled comparative sentences.</p> <p>The lexicon is a set of 176 words frequently used to express a comparative opinion in the Portuguese language. In these contexts, the lexicon is aggregated in a filter and used to build two sets of data with comparative sentences from two important contexts: (1) Social Network Online; and (2) Product reviews.</p> <p>For Twitter, we collected all Portuguese tweets published in Brazil on 2018/01/10 and filtered all tweets that contained at least one keyword present in the lexicon, obtaining 130,459 tweets. Our work is based on the sentence level. Thus, all sentences were extracted and a sample with 2,053 sentences was created, which was labeled for three human manuals, reaching an 83.2% agreement with Fleiss' Kappa coefficient. For Buscapé, a Brazilian website (https://www.buscape.com.br/) used to compare product prices on the web, the same methodology was conducted by creating a set of 2,754 labeled sentences, obtained from comments made in 2013. This dataset was labeled by three humans, reaching an agreement of 83.46% with the Fleiss Kappa coefficient.</p> <p>The Twitter dataset has 2,053 labeled sentences, of which 918 are comparative. The Buscapé dataset has 2,754 labeled sentences, of which 1,282 are comparative.</p> <p><strong>The datasets contain these labeled properties:</strong></p> <ul> <li> <p><em>text</em>: the sentence extracted from the review comment.</p> </li> <li> <p><em>entity_s1: </em>the first entity compared in the sentence<em>.</em></p> </li> <li> <p><em>entity_s2: </em>the second entity compared in the sentence.</p> </li> <li> <p><em>keyword: </em>the comparative keyword used in the sentence to express comparison.</p> </li> <li> <p><em>preferred_entity: </em>the preferred entity.</p> </li> <li> <p><em>id_start: </em>the keyword's initial position in the sentence.</p> </li> <li> <p><em>id_end: </em>the keyword's final position in the sentence.</p> </li> <li> <p><em>type: </em>the sentence label, which specifies whether the phrase is a comparison<em>.</em></p> </li> </ul> <p><strong>Additional Information:</strong></p> <p><em><strong>1 </strong>- </em>The sentences were separated using a sentence tokenizer.</p> <p><em><strong>2 </strong>- </em>If the compared entity is not specified, the field will receive a value: "__".</p> <p><em><strong>3</strong> - </em>The property <em>"type"</em> can contain five values, they are:</p> <ul> <li> <p><em>0: Non-comparative (</em>Não Comparativa<em>)</em>.</p> </li> <li> <p><em>1: Non-Equal-Gradable (</em>Gradativa com Predileção<em>)</em>.</p> </li> <li> <p><em>2: Equative</em> <em>(</em>Equitativa<em>).</em></p> </li> <li> <p><em>3: Superlative (</em>Superlativa<em>).</em></p> </li> <li> <p><em>4: Non-Equal-Gradable (</em>Não Gradativa<em>).</em></p> </li> </ul> <p> </p> <p>If you use this data, please cite our paper as follows: </p> <p><em>"Daniel Kansaon, Michele A. Brandão, Julio C. S. Reis, Matheus Barbosa,Breno Matos, and Fabrício Benevenuto. 2020. Mining Portuguese Comparative Sentences in Online Reviews. In Brazilian Symposium on Multimedia and the Web (WebMedia ’20), November 30-December 4, 2020, São Luís, Brazil. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3428658.3431081"</em></p> <p>--------------</p> <p><strong>Plus Information:</strong></p> <p>We make the raw sentences available in the dataset to allow future work to test different pre-processing steps. Then, if you want to obtain the exact sentences used in the paper above, you must reproduce the pre-processing step described in the paper (<em>Figure 2</em>). </p> <p>For each sentence with more than one keyword in the dataset: </p> <ul> <li>You need to extract three words before and three words after the comparative keyword, creating a new sentence that will receive the existing value in the “<em>type</em>” field as a label;</li> <li>The original sentence will be divided into <em>n</em> new sentences. (<em>n</em>) is the number of keywords in the sentence;</li> <li>The stopwords should not be accounted for as part of this range (<em>3 words</em>);</li> </ul> <p>Note that: the final processed sentence can have more than six words because the stopwords are not counted as part of the range.</p>
Images from Newspaper Navigator predicted as maps, with human corrected labels
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'maps'. In the original training data for Newspaper Navigator, there were relatively few labelled examples of maps. The predictions for maps have an <a href="https://github.com/LibraryOfCongress/newspaper-navigator">Average Precision of 69.5%, and 34 images in the validation data</a>.</p> <p>This dataset contains a sample of these images which have been predicted as 'maps'. It also includes additional labels which indicate whether the predicted map image is a 'map' or 'not a map'. </p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in 'newspaper_maps.zip' </li> <li>`2020_30_10_13_19_228_sample.json` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>map_labels.csv contains the labels for the images as a CSV file </li> </ul>
The ALeRCE Light Curve Classifier: labeled set, features, and classifications
<p>Labeled set, features, and classifications of the ZTF alert stream (up to 2020/06/09) presented in the article "Alert Classification for the ALeRCE Broker System: The Light Curve Classifier", Sánchez-Sáez et al., accepted for publication in the Astronomical Journal.</p> <p>The labeled set is used by the ALeRCE broker (<a href="http://alerce.science">alerce.science</a>) to provide updated classifications for sources with new ZTF alerts every day. To obtain updated classifications visit <a href="http://alerce.online">alerce.online</a>. In addition, more examples and instructions on how to use the ALeRCE database and classifications can be found on the <a href="http://alerce.science">alerce.science</a> website. </p> <p> </p> <p>File description:</p> <ul> <li>"labeled_set_lc_classifier_SanchezSaez_2020.csv" contains the labeled set described in Section 2.2 of the paper.</li> <li>"features_for_lc_classifier_20200609.csv" contains the features computed for the unlabeled ZTF set (up to 2020/06/09). The features are described in Section 3, and Appendix A of the paper.</li> <li>"ALeRCE_lc_classifier_outputs_ZTF_unlabeled_set_20200609.csv" contains the probabilities obtained for each level of the model, as well as the final predictions and probabilities (up to 2020/06/09). For more details about the model see Sections 4.1 and 5.3.</li> </ul> <p> </p> <p> </p>
MER Opportunity and Spirit Rovers Pancam Images Labeled Data Set
<p><strong>Introduction</strong></p> <p>The data set is based on 3,004 images collected by the Pancam instruments mounted on the Opportunity and Spirit rovers from NASA's Mars Exploration Rovers (MER) mission. We used rotation, skewing, and shearing augmentation methods to increase the total collection to 70,864 (see Image Augmentation section for more information). Based on the <a href="https://merdatacatalog.com/survey">MER Data Catalog User Survey</a> [1], we identified 25 classes of both scientific (e.g. soil trench, float rocks, etc.) and engineering (e.g. rover deck, Pancam calibration target, etc.) interests (see Classes section for more information). The 3,004 images were labeled on <a href="https://www.zooniverse.org/">Zooniverse platform</a>, and each image is allowed to be assigned with multiple labels. The images are either 512 x 512 or 1024 x 1024 pixels in size (see Image Sampling section for more information).</p> <p><strong>Classes</strong></p> <p>There is a total of 25 classes for this data set. See the list below for class names, counts, and percentages (the percentages are computed as count divided by 3,004). Note that the total counts don't sum up to 3,004 and the percentages don't sum up to 1.0 because each image may be assigned with more than one class. </p> <ul> <li>Class name, count, percentage of dataset</li> <li>Rover Deck, 222, 7.39%</li> <li>Pancam Calibration Target, 14, 0.47%</li> <li>Arm Hardware, 4, 0.13%</li> <li>Other Hardware, 116, 3.86%</li> <li>Rover Tracks, 301, 10.02%</li> <li>Soil Trench, 34, 1.13%</li> <li>RAT Brushed Target, 17, 0.57%</li> <li>RAT Hole, 30, 1.00%</li> <li>Rock Outcrop, 1915, 63.75%</li> <li>Float Rocks, 860, 28.63%</li> <li>Clasts, 1676, 55.79%</li> <li>Rocks (misc), 249, 8.29%</li> <li>Bright Soil, 122, 4.06%</li> <li>Dunes/Ripples, 1000, 33.29%</li> <li>Rock (Linear Features), 943, 31.39%</li> <li>Rock (Round Features), 219, 7.29%</li> <li>Soil, 2891, 96.24%</li> <li>Astronomy, 12, 0.40%</li> <li>Spherules, 868, 28.89%</li> <li>Distant Vista, 903, 30.23%</li> <li>Sky, 954, 31.76%</li> <li>Close-up Rock, 23, 0.77%</li> <li>Nearby Surface, 2006, 66.78%</li> <li>Rover Parts, 301, 10.02%</li> <li>Artifacts, 28, 0.93%</li> </ul> <p><strong>Image Sampling</strong></p> <p>Images in the MER rover Pancam archive are of sizes ranging from 64x64 to 1024x1024 pixels. The largest size, 1024x1024, was by far the most common size in the archive. For the deep learning dataset, we elected to sample only 1024x1024 and 512x512 images as the higher resolution would be beneficial to feature extraction.</p> <p>In order to ensure that the data set is representative of the total image archive of 4.3 million images, we elected to sample via "site code". Each Pancam image has a corresponding two-digit alphanumeric "site code" which is used to track location throughout its mission. Since each "site code" corresponds to a different general location, sampling a fixed proportion of images taken from each site ensure that the data set contained some images from each location. In this way, we could ensure that a model performing well on this dataset would generalize well to the unlabeled archive data as a whole. We randomly sampled 20% of the images at each site within the subset of Pancam data fitting all other image criteria, applying a floor function to non-whole number sample sizes, resulting in a dataset of 3,004 images.</p> <p><strong>Train/validation/test sets split</strong></p> <p>The 3,004 images were split into train, validation, and test data sets. The split was done so that roughly 60, 15, and 25 percent of the 3,004 images would end up as train, validation, and test data sets respectively, while ensuing that images from a given site are not split between train/validaiton/test data sets. This resulted in 1,806 train images, 456 validation images, and 742 test images. </p> <p><strong>Augmentation</strong></p> <p>To augment the images in train and validation data sets (note that images in the test data set were not augmented), three augmentation methods were chosen that best represent transformations that could be realistically seen in Pancam images. The three augmentations methods are rotation, skew, and shear. The augmentation methods were applied with random magnitude, followed by a random horizontal flipping, to create 30 augmented images for each image. Since each transformation is followed by a square crop in order to keep input shape consistent, we had to constrict the magnitude limits of each augmentation to avoid cropping out important features at the edges of input images. Thus, rotations were limited to 15 degrees in either direction, the 3-dimensional skew was limited to 45 degrees in any direction, and shearing was limited to 10 degrees in either direction. Note that augmentation was done only on training and validation images. </p> <p><strong>Directory Contents</strong></p> <ul> <li>images: contains all 70,864 images</li> <li>train-set-v1.1.0.txt: label file for the training data set</li> <li>val-set-v1.1.0.txt: label file for the validation data set</li> <li>test-set-v1.1.0.txt: label file for the testing data set</li> </ul> <p>Images with relatively short file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg) are original images, and images with long file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg_04140167-5781-49bd-a913-6d4d0a61dab1.jpg) are augmented images. The label files are formatted as "Image name, Class1, Class2, ..., ClassN".</p> <p> </p> <p><strong>Reference</strong></p> <p>[1] S.B. Cole, J.C. Aubele, B.A. Cohen, S.M. Milkovich, and S.A. Shields, Identifying Community Needs for a Mars Exploration Rovers (MER), Daata Catalog, 51st Lunar and Planetary Science Conference (LPSC), 2020.</p>
Biodiversity Hotspots Map (English labels)
<p>This map displays the global <a href="https://zenodo.org/record/3261807#.X8_HgNhKg2x">Biodiversity Hotspots 2016.1 dataset</a>. The colors assigned to the hotspots are only used to distinguish adjacent hotspots and have no other meaning. The biodiversity hotspots represent terrestrial biodiversity only. The offshore lines are a cartographic device to group and highlight islands that are part of the same hotspots (e.g. Polynesia-Micronesia, Indo-Burma). The background image is from Natural Earth. This map is labelled with hotspot names in English; a version without text is also available: 10.5281/zenodo.4311831. </p> <p>There are currently <a href="https://www.cepf.net/node/1996">36 recognized biodiversity hotspots</a>. These are Earth’s most biologically rich—yet threatened—terrestrial regions.</p> <p>To qualify as a biodiversity hotspot, an area must meet two strict criteria:</p> <ul> <li>Contain at least 1,500 species of vascular plants found nowhere else on Earth (known as "endemic" species).</li> <li>Have lost at least 70 percent of its primary native vegetation.</li> </ul> <p>Many of the biodiversity hotspots exceed the two criteria. For example, both the Sundaland Hotspot in Southeast Asia and the Tropical Andes Hotspot in South America have about <strong>15,000</strong> endemic plant species. The loss of vegetation in some hotspots has reached a startling <strong>95</strong> percent.</p>
Deep-tissue label-free quantitative optical tomography
<p>Deep-tissue label-free quantitative optical tomography</p> <p>Jelle van der Horst, Anna K. Trull, and Jeroen Kalkman</p> <p>The data sets, software, and figures accompany the publication:</p> <p>"Deep-tissue label-free quantitative optical tomography", Optica 7, 1682 (2020) <a href="https://doi.org/10.1364/OPTICA.397549">https://doi.org/10.1364/OPTICA.397549</a></p> <ul> <li>calc_hilbert_cor.m, corrects OCT data for dispersion</li> <li>cascade_measurement.m, analysis of OCT reference data<\li></li> <li>data_A2_polyfit_v1_slice_0198.zip, Peak fitted data for all 200 slices (200 files)<\li></li> <li>Ex18-11-01B, spectral-domain OCT reference data<\li></li> <li>Ex18-11-01D, spectral-domain OCT reference data for background<\li></li> <li>Ex18-11-01E_sweep_0001.dat, Reference power distribution of a sweep<\li></li> <li>loadframes_refdata.m, load spectral-domain OCT data<\li></li> <li>manual_COR_correction.m, perform manual center of rotation correction<\li></li> <li>run_frame_analysis_polyfit.m, fit spatial-domain OCT data<\li></li> <li>run_tomo_ocpt.m, main MATLAB file for the reconstruction <\li></li> <li>shift_correction_galvo.m, correction for galvo offset<\li></li> </ul>
Swiss3DCities: Aerial Photogrammetric 3D Pointcloud Dataset with Semantic Labels
<p>We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 km<sup>2</sup>, sampled from three Swiss cities with different characteristics. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras. In contrast to datasets acquired with ground LiDAR sensors, the resulting point clouds are uniformly dense and complete, and are useful to disparate applications, including autonomous driving, gaming, smart city planning, and robotics.</p>
Street view images of building with materials labeled
<p>Street view images of building with materials labeled: concrete, masonry, wood</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, SE1-SE6)
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p> <p>Raw image data for super enhancers SE1 to SE6</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, genes)
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p> <p>Raw image data for four genes: rnf19a, cdc25b, celf1, crsp7</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, SE7-SE12)
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p> <p>Raw image data for super enhancers SE7 to SE12</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p>
Dataset supporting the manuscript dedicated to lignin precursor incorporation analysis, bioorthogonal labeling, parametric and AI based segmentation.
<p>This dataset aims to test the algorithms presented in an article submitted by the authors and untitled:</p> <p><strong>The combination of chemical reporter-, segmentation- and ratiometric-methods enables high-quality mapping of lignification dynamics in plant cell walls</strong></p> <p><strong>Are available:</strong></p> <p>-the algorithm with graphical user interface for imageJ and its installation procedure ("Cell_Wall_Segmentation " and "Tutorial Cell_Wall_Segmentation")</p> <p>-a folder comprising a classifier and a data set compatible with the machine learning part of the algorithm "data and classifier for weka"</p> <p>- representative images adapted for testing “representative images”</p> <p>- the macro corresponding to the parametric segmentation procedure (see imageJ documentation for installation instructions) “parametric_segmentation”</p>
Data from: Single-dose oral ciprofloxacin prophylaxis as a response to a meningococcal meningitis epidemic in the African meningitis belt: a three-arm, open-label, cluster-randomized trial
Background: Antibiotic prophylaxis for contacts of meningitis cases is not recommended during outbreaks in the African meningitis belt. We assessed the effectiveness of single-dose oral ciprofloxacin administered to household contacts and in village-wide distributions on the overall attack rate (AR) in an outbreak of meningococcal meningitis. Methods and findings: In this 3-arm, open-label, cluster-randomized trial during a meningococcal meningitis outbreak in Madarounfa District, Niger, villages notifying a suspected case were randomly assigned (1:1:1) to standard care (the control arm), single-dose oral ciprofloxacin for household contacts within 24 hours of case notification, or village-wide distribution of ciprofloxacin within 72 hours of first case notification. The primary outcome was the overall AR of suspected meningitis after inclusion. A random sample of 20 participating villages was enrolled to document any changes in fecal carriage prevalence of ciprofloxacin-resistant and extended-spectrum beta-lactamase (ESBL)–producing Enterobacteriaceae before and after the intervention. Between April 22 and May 18, 2017, 49 villages were included: 17 to the control arm, 17 to household prophylaxis, and 15 to village-wide prophylaxis. A total of 248 cases were notified in the study after the index cases. The AR was 451 per 100,000 persons in the control arm, 386 per 100,000 persons in the household prophylaxis arm (t test versus control p = 0.68), and 190 per 100,000 persons in the village-wide prophylaxis arm (t test versus control p = 0.032). The adjusted AR ratio between the household prophylaxis arm and the control arm was 0.94 (95% CI 0.52–1.73, p = 0.85), and the adjusted AR ratio between the village-wide prophylaxis arm and the control arm was 0.40 (95% CI 0.19‒0.87, p = 0.022). No adverse events were notified. Baseline carriage prevalence of ciprofloxacin-resistant Enterobacteriaceae was 95% and of ESBL-producing Enterobacteriaceae was >90%, and did not change post-intervention. One limitation of the study was the small number of cerebrospinal fluid samples sent for confirmatory testing. Conclusions: Village-wide distribution of single-dose oral ciprofloxacin within 72 hours of case notification reduced overall meningitis AR. Distributions of ciprofloxacin could be an effective tool in future meningitis outbreak responses, but further studies investigating length of protection, effectiveness in urban settings, and potential impact on antimicrobial resistance patterns should be carried out.
Data from: Ophthalmologic evaluation of severely obese patients undergoing bariatric surgery: a pilot, monocentric, prospective, open-label study
Purpose: The aim of this study was to investigate the pathogenic role of obesity on blinding eye diseases in a population of severely obese patients with no history of eye diseases, and to verify whether weight loss induced by bariatric surgery may have a protective effect. Methods: This was a pilot, monocentric, prospective, and open label study conducted at the University Hospital of Pisa. Fifty-seven severely obese patients with a mean body mass index value of 44.1 ± 6 kg/m2 were consecutively recruited and received a complete ophthalmological evaluation and optical coherence tomography. Twenty-nine patients who underwent gastric bypass were evaluated also 3 months, and 1 year after surgery. Results: At baseline, blood pressure value were directly and significantly related to intraocular pressure values (p<0.05, R = 0.35). Blood pressure values were also significantly and inversely related to retinal nerve fiber layer thickness, particularly in the temporal sector (RE p<0.05 r-0.30; LE p<0.01, R = -0.43). Moreover, minimum foveal thickness values were significantly and inversely associated with body mass index (RE p<0.02, R = -0.40; LE p<0.02, R = -0.30). A significant reduction of body mass index (p<0.05) and a significant (p<0.05) improvement of blood pressure was observed three months and one year after gastric bypass, which were significantly associated with an increase in retinal nerve fiber layer thickness and minimum foveal thickness values in both eyes (p<0.05). Conclusions: The results of this study suggest that obese patients may have a greater susceptibility to develop glaucomatous optic nerve head damage and age-related macular degeneration. Moreover, weight reduction and improvement of comorbidities obtained by bariatric surgery may be effective in preventing eye disease development by improving retinal nerve fiber layer and foveal thickness.
Data from: Novel reverse radioisotope labelling experiment reveals carbon assimilation of marine calcifiers under ocean acidification conditions
<p><span>1. Ocean acidification by anthropogenic carbon dioxide emissions is projected to depress metabolic and physiological activity in marine calcifiers. To evaluate the sensitivity of marine organisms against ocean acidification, the assimilation of nutrients into carbonate shells and soft tissues must be examined.</span></p> <p><span>2. We designed a novel experimental protocol, reverse radioisotope labelling, to trace partitioning of nutrients within a single bivalve species under ocean acidification conditions. Injecting CO<sub>2</sub> gas, free from radiocarbon, can provide a large contrast between carbon dissolved in the water and the one assimilated from atmosphere. By culturing modern aquifer organisms in acidified seawater, we were able to determine differences in the relative contributions of the end members, dissolved inorganic carbon (DIC) in seawater and metabolic CO<sub>2</sub>, to shell carbonate and soft tissues. </span></p> <p><span>3. Under all pCO<sub>2</sub> conditions (463, 653, 872, 1137, and 1337 μatm), radiocarbon (Δ<sup>14</sup>C) values of the bivalve (<i>Scapharca broughtonii</i>) shell were significantly correlated with seawater DIC values; therefore, shell carbonate was derived principally from seawater DIC. The Δ<sup>14</sup>C results together with stable carbon isotope (δ<sup>13</sup>C) data suggest that in <i>S. broughtonii</i> shell δ<sup>13</sup>C may reflect the kinetics of isotopic equilibration as well as end-member contributions; thus, care must be taken when analyzing end-member contributions by a previous method using δ<sup>13</sup>C. The insensitivity of <i>S. broughtonii</i> to perturbations in pCO<sub>2</sub> up to at least 1337 µatm indicates that this species can withstand ocean acidification.</span></p> <p><span>4. Usage of radioisotope to dope for tracer experiments requires strict rules to conduct any operations. Yet, reverse radioisotope labeling proposing in this study has a large advantage and is a powerful tool to understanding physiology of aquifer organisms that can be applicable to various organisms and culture experiments, such as temperature, salinity, and acidification experiments, to improve understanding of the proportions of nutrients taken in by marine organisms under changing environments.</span></p>
Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics - Data Supplement
<p>This the is Data Supplement for the article "Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics" submitted to the Journal of Proteomics 2015.</p>
PLATE I. Fig. 1 in Pentatomoidea (Hemiptera: Heteroptera) wrongly labelled in Gay's " Atlas de la Historia Física y Política de Chile " (1854)
PLATE I. Fig. 1 Acledra haematopa (Spinola, 1852). Head, lateral view; Fig. 2. Parajalla sanguineosignata (Spinola, 1852). Head, lateral view.
FIGURES 32–38. Flatovertex rufotibialis Zheng, 1981, holotype male. 32. Dorsal view. 33. Lateral view. 34. Labels. 35 in Taxonomic notes on Pternoscirta pulchripes Uvarov, 1925 (Orthoptera: Acrididae: Oedipodinae) with proposal of new synonyms in the genera Flatovertex and Mioscirtus
FIGURES 32–38. Flatovertex rufotibialis Zheng, 1981, holotype male. 32. Dorsal view. 33. Lateral view. 34. Labels. 35. Inner surface of hind femur. 36. Outer surface of hind femur. 37–38. Closeups of the premedial and posterior portions of hind femur showing the extremely fine denticles on the upper median keel.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.