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Dataset results
311 results for “new dataset”
Dataset for Measurement report: Ion clusters as indicator for local new particle formation
<p>Data for Measurement report: Ion clusters as indicator for local new particle formation. There are two files, negative_ion_concentrations.csv and positive_ion_concentrations.csv. The former (latter) includes absolute number concentrations for 1.87, 2.16, 2.49, and 2.88 nm negative (positive) ions. The unit for these concentrations is #/cm<sup>-3</sup>.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
High-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category: Dataset and Code
<p>Dataset of identified high-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category, as well as the associated code to reproduce the figures of accompanying publication. The data have been recorded by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Array. </p><p><i>Accompanying publication:</i> Taenzer, L.L., Gawarkiewicz, G., and Plueddemann, A. (2023). Categorization of High-Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf. Journal of Geophysical Research: Oceans, 128, e2022JC019625. https://doi.org/10.1029/2022JC019625</p><p><i>Contact:</i> Lukas Taenzer (lukas.taenzer@whoi.edu)</p><p><strong>Structure of provided code:</strong></p><ul><li>PART A: Local high-wind ocean impact analysis</li><li>PART B: Analysis of seasonal high-wind impacts on stratification</li><li>PART C: High-wind event categorization and the impact of different categories</li></ul><p>Code has been written in MATLAB R2023a.</p><p><strong>Output:</strong></p><ul><li>Processed data of all locally detected high-wind events incl. scalar forcing and shelf impact estimates as well as their corresponding high-wind event category:<ul><li>'OOIcp_HighWindEvents_ScalarMetrics.nc' (see userflag 'save_peak_ooi')</li><li>See README_HighWindEvents_ScalarMetrics for further details and license.</li></ul></li><li>Figures 2, 3, 4, 5, 6, 7, 8, and 9 of accompanying publication<ul><li>saved as .png file (always)</li><li>saves as .eps file (see userflag 'save_fig_eps')</li></ul></li></ul><p><strong>Input for Analysis:</strong></p><ul><li>Gridded Hydrography and Bulk Air-Sea interactions time series observed by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) (Taenzer et al., 2023). The required fields to reproduce the results of the accompanying publication are provided:<ul><li>Input/OOIcp_Met_Combined.nc</li><li>Input/OOIcp_CTD_ISSM_stat.nc</li><li>Input/OOIcp_CTD_PMUI_prof.nc</li></ul></li><li>High-wind event categorization based on their spatio-temporal sea level pressure and temporal surface wind stress signatures around/at the OOI Coastal Pioneer Array location:<ul><li>Input/storm_type_2015-2021_v5.mat</li></ul></li></ul><p><strong>Additional input for reproducing figures:</strong></p><ul><li>Manually determined cyclone tracks for cyclones that occur during the fall destratification seasons 2015-2021:<ul><li>Input/stormtracks_cyclones_20152021_save.mat</li></ul></li><li>ERA5 sea level pressure data (Hersbach et al., 2018) on a 6-hour temporal and a 1°x1° spatial resolution for the time period 2015-01-01 to 2022-06-30 and across the Eastern US, Canada, and the Northwest Atlantic with the OOI Coastal Pioneer Array in the center<ul><li>Input/ERA5_6h_2015-2022_region_1x1.mat</li></ul></li></ul>
Dataset of fungal communities observed on decomposing pig carcasses in New Jersey
<p>This dataset contains estimated count data for fungal taxa identified using ITS metabarcoding collected from decomposing fetal pig carcasses placed in grasslands of New Jersey, USA.</p> <p>FungiPigDecomp_Data.csv is a file that contains the estimated count data at the level of taxonomic resolution possible for each replicate, at each stage of decomposition, across three body districts.</p> <p>FungiPigDecomp_Methods.docx is a summarized version of the sampling method relevant to interpreting the data.</p> <p>FungiPigDecomp_Descriptive.txt is a file describing the column headers in "FungiPigDecomp_Data.csv".</p>
Dataset for "A new method for identifying weather-induced power system stress using shadow prices"
<p>These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of</p> <ul> <li>solved network files (generated with <a href="https://github.com/PyPSA/pypsa-eur/">PyPSA-Eur</a>, here v0.6.1), used for the analysis,</li> <li>necessary data to reproduce the figures in the paper and supplementary material.</li> </ul> <p>The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`.</p> <p>The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files.</p> <p>Find more information about how to use these data and how they were generated in the README of the GitHub repository: <a href="https://github.com/koen-vg/stressful-weather/tree/v0">https://github.com/koen-vg/stressful-weather/tree/v0</a>.</p>
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance inTwitter Conversations
<p>Automated ways to extract stance (denying vs. supporting opinions) from conversations on social media are essential to advance opinion mining research. Recently, there is a renewed excitement in the field as we see new models attempting to improve the state-of-the-art. However, for training and evaluating the models, the datasets used are often small. Additionally, these small datasets have uneven class distributions, i.e., only a tiny fraction of the examples in the dataset have favoring and denying stance, and most other examples have no clear stance. Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e.g., replying vs. quoting on Twitter). Because of this, models trained on one event do not generalize to other events. </p> <p>In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues. To the best of our knowledge, this is currently the largest human-labeled stance dataset for Twitter conversations with over 5200 stance labels. More importantly, we designed a tweet collection methodology that favours the selection of denial-type responses. This class is expected to be more useful in the identification of rumours and determining antagonistic relationships between users. </p>
ISPON: A New Dataset for Identifying Sources in Political Online News
<p>This dataset contains a set of annotations for informational news sources (such as eyewitnesses, public officials, academic experts, reports, or other documentation) that provide support for claims made within online political news articles. Our dataset contains fine-grained annotations on the sources cited within each article, including in-text notations highlighting the words or phrases signaling a source. The dataset comprises annotations for nearly 2,500 articles covering 47 outlets. In addition, the dataset includes a larger set of >150,000 URLs from 92 outlets.</p>
Dataset: RC U-shaped walls subjected to in-plane, diagonal, and torsional loading: new experimental findings
<p>This dataset contains the processed experimental digital image correlation (DIC) technique data for two large-scale reinforced concrete U-shaped wall specimens tested at the Earthquake Engineering and Structural Dynamics Laboratory (EESD Lab), École Polytechnique Féderale de Lausanne (EPFL) in Switzerland. The abstract for the corresponding journal paper, submitted to <em>Engineering Structures</em>, is given below.</p> <p>Although reinforced concrete U-shaped walls are popular in construction practice internationally, there is a paucity of experimental research investigating the seismic performance of such salient elements. The present paper summarizes an experimental campaign on two slender U-shaped reinforced concrete walls detailed with a single-layer of reinforcement. State-of-the-art instrumentation was used to capture the three-dimensional displacement field of the wall surfaces using digital image correlation techniques. Experimental findings are presented, including strain profiles, equivalent plastic hinge lengths, longitudinal strains at the base, cracking distributions and widths, and out-of-plane deformations. The longitudinal strain profiles showed a yielding region up the boundary ends of the flanges of approximately 800 mm to 1200 mm in length, depending on the direction of loading and at large drift levels. Approximately half of the yielding zone length was found to be equal to the equivalent plastic hinge lengths, which were found to decrease as a function of drift. The longitudinal strains at the base of these walls showed some shear lag effects when subjected to in-plane or diagonal loading. For most directions of loading, the largest crack widths were found to be associated with flexural-shear or shear cracks. When subjected to a pure torque, the vertical strain distribution at the base of the wall correlated with the theoretical distribution for an open section governed by warping torsion. The out-of-plane deformations were primarily concentrated within a small region towards the ends of the flanges prior to the local buckling failures that were observed experimentally.Although reinforced concrete U-shaped walls are popular in construction practice internationally, there is a paucity of experimental research investigating the seismic performance of such salient elements. The present paper summarizes an experimental campaign on two slender U-shaped reinforced concrete walls detailed with a single-layer of reinforcement. State-of-the-art instrumentation was used to capture the three-dimensional displacement field of the wall surfaces using digital image correlation techniques. Experimental findings are presented, including strain profiles, equivalent plastic hinge lengths, longitudinal strains at the base, cracking distributions and widths, and out-of-plane deformations. The longitudinal strain profiles showed a yielding region up the boundary ends of the flanges of approximately 800 mm to 1200 mm in length, depending on the direction of loading and at large drift levels. Approximately half of the yielding zone length was found to be equal to the equivalent plastic hinge lengths, which were found to decrease as a function of drift. The longitudinal strains at the base of these walls showed some shear lag effects when subjected to in-plane or diagonal loading. For most directions of loading, the largest crack widths were found to be associated with flexural-shear or shear cracks. When subjected to a pure torque, the vertical strain distribution at the base of the wall correlated with the theoretical distribution for an open section governed by warping torsion. The out-of-plane deformations were primarily concentrated within a small region towards the ends of the flanges prior to the local buckling failures that were observed experimentally.</p>
dataset for paper Vanhaebost J, Faouzi M, Mangin P, Michaud K: New reference tables and user-friendly Internet application for predicted heart weights. Int J Legal Med 2014, 128(4):615-620.
<p>The heart weight is the most important parameter in the determination of cardiac hypertrophy. The obtained heart weight value should be compared against tables of normal weights by age, gender and body weight and height</p> <p>In the study by Vanhaebost<em> et al</em>. has been shown in the Swiss population that the heart weight increases along with the increase of the body weight, body height, BMI and body surface area (BSA). The mean heart weight is greater in men than in women at a similar body weight. The reference tables for predicted heart weights obtained from this study are presented as an user-friendly internet application (<a href="http://calc.chuv.ch/Heartweight">http://calc.chuv.ch/Heartweight</a>) enabling the comparison of heart weights observed at autopsy with the reference values.</p>
A new dataset of global irrigation areas from 2000 to 2015
<pre>We provide global irrigation maps README FOR GLOBAL IRRIGATION MAPS --------------------------------- Prediction Maps --------------- v3b_combined_*.tif: GeoTIFF files with model predictions, from 2001 to 2015. 0=not irrigated, 1=low-to-medium irrigated, 2=highly irrigated. Two of these are available in PNG format as well: 2001, 2015 Difference Between 2001 and 2015 -------------------------------- diff2001vs2015.tif: 0=no difference, 1=large decrease, 2=decrease, 3=no change, 4=increase, 5=large increase Also available in PNG format. Dark green=large decrease, green=decrease, grey=no change, orange=increase, red=large increase Assessment Map -------------- assessment_map.tif: FN=false negatives, FP=false positives, TP=true positives, mask=cropland mask</pre>
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</p>
Dataset for project Lipobodies, related to the development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction
<p>This dataset contains primary (including raw data) that supports the results of the design and development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction</p>
Fig. 11. Bayesian inference trees. A. 16S rRNA dataset. B. Cytochrome oxidase I in Designation of a neotype for Myxicola infundibulum (Montagu, 1808) (Annelida: Sabellidae) and a new species from the UK
Fig. 11. Bayesian inference trees. A. 16S rRNA dataset. B. Cytochrome oxidase I gene dataset. The first value at each node represents maximum likelihood bootstrap support, the second the Bayesian posterior probabilities and the third the maximum parsimony bootstrap support.
A re-analysis of an existing Drosophila melanogaster dataset reveals a new set of genes involved in post-mating response
<p>The figures and tables presented here are part of a manuscript submitted for publication by Chloe J. Bennett and Rodolfo Aramayo entitled:</p> <p><strong>"A re-Analysis of an existing <em>Drosophila melanogaster</em> dataset reveals a new set of genes involved in post-mating response"</strong></p> <p><strong>Abstract</strong></p> <p>RNA sequencing (RNA-seq) is a commonly used method to identify changes in gene expression between two conditions. The analysis of RNA-seq output is complicated, with the possibility of getting different results from the same raw data. We developed and deployed four parallel pipelines to reanalyze an existing dataset of two female Drosophila melanogaster tissue types before and after mating. The Drosophila post-mating response (PMR) is a well-characterized suite of changes that occur after mating, accompanied by a flux in gene expression. In comparing our study with the previous analysis of this dataset, we find our results to be more stringent, though we do identify a number of significant genes not found before. We also found variation among our own separate experiments, with gene-to-transcript isoform number and index building playing important roles in outcome. Finally, we identified a set of genes found by our pipeline that were not identified by the previous study and proposed potential roles for these genes in post-mating biology. Together, this work presents a critique of current RNA-seq analysis techniques and proposes multiple workflow adjustments that can increase the sensitivity, specificity, and stringency of differential gene expression studies.</p>
Dataset accompanying "A new species of Mesolepis from the Late Carboniferous of Scotland, with especial reference to Mesolepis wardi Young"
<p>This dataset accompanies the manuscript "A new species of <em>Mesolepis </em>from the Late Carboniferous of Scotland, with especial reference to <em>Mesolepis wardi </em>Young" (<span><a href="https://doi.org/10.1017/S1755691024000094" target="_blank" rel="noopener">https://doi.org/10.1017/S1755691024000094</a>)</span> and comprises the following items: </p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/1 (part) raw data (TIFF stack, zipped)</p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/1 (part) .mcs file</p> <p><em>- Mesolepis arabellae</em> GLAHM 163398/1 (part) .ply files (zipped)</p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/2 (counterpart) raw data (TIFF stack, zipped)</p> <p><em>- Mesolepis arabellae</em> GLAHM 163398/2 (counterpart).mcs file</p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/2 (counterpart) .ply files (zipped)</p> <p>- <em>Mesolepis arabellae </em>GLAHM 163398/2 (fin region) raw data (TIFF stack, zipped)</p> <p><em>- Mesolepis arabellae</em> GLAHM 163398/2 (fin region) .mcs file</p> <p>- <em>Mesolepis arabellae </em>GLAHM 163398/2 (fin region) .ply file</p>
Mobilising marine biodiversity data: a new malacological dataset of Italian records (Mollusca)
<p>The location and palaeoceanographic history of the Mediterranean Sea make it a biodiversity hotspot, prompting extensive studies in this region. However, despite the marine biodiversity of this area is apparently widely studied, a large amount of distributional data for Mediterranean taxa is still unpublished or scattered in various sources and formats, causing severe limitations to their potential reuse. This emerges as a particularly thorny issue for highly biodiverse and neglected taxa, such as invertebrates. The mobilisation of these frozen data through a process of standardisation and georeferencing could potentially support biodiversity research and conservation. The aim of this work is to provide a standardised pipeline to integrate these dispersed data, focusing on the Italian waters of the Mediterranean Sea and using molluscs as target taxa. Data were gathered from two main sources: published literature and Natural History Collections. The harmonisation process involved three key steps: 1) terminology and structure standardisation, 2) taxonomy updating and 3) georeferencing. Our efforts yielded over 44000 standardised records of mollusc species from Italian seawaters. These records encompassed primary biodiversity data from newly digitised specimens owned by 11 different institutions and private collectors, as well as secondary biodiversity data extracted from 311 published studies.</p>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
Improving 30-meter global impervious surface area (GISA) mapping: New method and dataset
<p>Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (<30 m) over long time spans (>30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from omission and commission issues, affecting their accuracy in practical applications. To address these challenges, we proposed a novel global longterm ISA mapping method and generated a new 30 m global ISA dataset from 1985 to 2021, namely GISA-new. Specifically, to reduce ISA omissions, a multi-temporal Continuous Change Detection and Classification (CCDC) algorithm that accounts for newly added ISA regions (NA-CCDC) was proposed to enhance the diversity and representativeness of the training samples. Meanwhile, a multi-scale iterative (MIA) method was proposed to automatically remove global commissions of various sizes and types. Finally, we collected two independent test datasets with over 100,000 test samples globally for accuracy assessment. Results showed that GISA-new out performed other existing global ISA datasets, such as GISA, WSF-evo, GAIA, and GAUD, achieving the highest overall accuracy (93.12 %), the lowest omission errors (10.50 %), and the lowest commission errors (3.52 %). Furthermore, the spatial distribution of global ISA omissions and commissions was analyzed, revealing more mapping uncertainties in the Northern Hemisphere. In general, the proposed method in this study effectively addressed global ISA omissions and removed commissions at different scales. The generated high-quality GISAnew can serve as a fundamental parameter for a more comprehensive understanding of global urbanization.</p>
Dataset for ´´A New Detailed Global Map of Lunar Light Plains´´ research article
<p>The shapefiles (.shp) provided in this repository are the datasets for the paper ´A new detailed global map of lunar light plains´ published in PSJ journal Special Issue. </p> <p>These shapefiles can be directly imported in ArcMap/ArcPRO. The third dataset is a .tif or image of the global map for a fast and easy overview.</p> <p>Two geomorphologic maps of lunar light plains are provided as described in the article: one with an FeO wt% cut off of about 12 wt% (Area_lightplains), and the other around 8 wt% (Area_LPFeOLow). </p> <p> </p>
Dataset for "Evaluation of Publicly Available Information on Sex-related Differences in the Efficacy and Safety of New Molecular Entities and Therapeutic Biological Products"
<p>Contains our extraction sheets with additional documents/notes on methods used in our study.</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.