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Tweets informing about resource needs and availabilities in post-disaster situation
<p>This is the dataset for the paper:</p> <p>Moumita Basu, Anurag Shandilya, Prannay Khosla, Kripabandhu Ghosh, Saptarshi Ghosh. Extracting Resource Needs and Availabilities from Microblogs for Aiding Post-Disaster Relief Operations. IEEE Transactions on Computational Social Systems, 2019.</p> <p>The data contains tweetids of tweets (from Twitter) posted during (1) the 2015 Nepal earthquake, and (2) 2016 Italy earthquake. The tweets that inform about need and availability of various types of resources are identified.</p> <p>The dataset can be used for developing algorithms for microblog retrieval / classification, and for understanding social media activity in the aftermath of a disaster event.</p>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
Digital Humanities in Brazil: the path of a growing culture of information visibility
<p>3rd lecture</p>
TD L2 Perfectionnement Evaluation de l'information sur Internet
<p>Scénario pédagogique et contenus destinés aux formateurs, pour assurer des séances de formations de 2h pour des étudiants de L2 des UFR Lettres Langues, et Arts et Sciences Humaines.</p> <p>Nous recevons les étudiants en demi-groupes de TD, soit une vingtaine d'étudiants maximum par séance.</p> <p>Nous introduisons la séance par un Kahoot qui permet de voir où en sont les étudiants concernant les grandes notions liées à la recherche et à l'évaluation sur Internet. Le Kahoot est très apprécié par les formateurs et par les étudiants car il permet, sur un mode ludique, d'instaurer une interaction, un dialogue, entre les deux parties dès le début de la séance.</p>
Channel State Information (CSI) analysis for predictive maintenance using Convolutional Neural Network (CNN)
<p>Dataset manual:</p> <p>This dataset contains CSI amplitude values for rotating motors in an office environment. Details of the experiments may be found in the corresponding paper published in the DATA'19 workshop, SenSys (<a href="https://doi.org/10.1145/3359427.3361917">https://doi.org/10.1145/3359427.3361917</a>). </p> <p>Folder structure:<br> The folders for servo motor and stepper motor contains separate folders for network reconnection conditions (w_recc: with reconnections, wo_recc: without recconnections) and load conditions (w_load: with load and wo_load: without load). The data is stores as Matlab files with .mat extentions. </p> <p>File structure:<br> In each file name, the digits after the '_' at the end of the file name correspond to the speed of the motor. In case of stepper motor these numbers could be directly interpreted as rpm. Ex: table_inj_with_load_5_0.mat corresponds to stationary motor (0 rpm) and table_inj_with_load_5_250.mat corresponds to motor rotating with 250 rpm speed. In the case of servo motor these numbers should be mapped with the following table in order to get the speeds.</p> <p>0: 0 rpm<br> 50: 14.45 rpm<br> 100: 8.02 rpm<br> 150: 5.38 rpm<br> 200: 4.05 rpm<br> 250: 3.26 rpm<br> 300: 2.67 rpm<br> Ex: table_inj_with_load_50.mat corresponds to motor running with 14.45 rpm.</p> <p>Each file has 3 columns, each corresponding to CSI value, labels (speed/last digits in the file name) and the data sample number (not in sequence as a result of packer loss) respectively. CSI values are typically a matrix of size 3000*180 (3000 CSI samples for 3sec data @1kHz sampling rate and 180 channels for 6 antenna pairs @ 30 subcarrier data per antenna).</p>
sr516/iSALE_PSS_data: PSS supporting information.
<p>Additional supporting information. Data from the model simulations used in Raducan, S. D., Davison, T. M., Collins, G. S. (2019) The effects of asteroid layering on ejecta mass-velocity distribution and implications for impact momentum transfer. Planetary and Space Science.</p>
Support information for the paper: First ten months of TGF observations by ASIM
<p>23 files with data are uploaded, and description of data files can be found in </p> <p>AGUSupporting-Information_Ostgaard_asim_first_ten_months.docx (also uploaded)</p>
Underlying data of the project "Are CONSORT checklists submitted by authors adequately reflecting what information is actually reported in published papers?"
<p><em><strong>Supplementary file 3.xlsx</strong></em> contains the evaluations for the 12 papers included in the study.</p>
Supplementary information to "Rainbow in the dark. The identification of diagnostic projectile impact features on rock crystal"
<p>Images of the experimental projectiles (pre-hafting, hafted before and after shooting), Hirox (Low magnification) pictures of pre-hafting and post-shooting projectiles and selection of fractures smoked with magnesium.</p>
Thermographic data information
<p>Data used for a published paper QJIRT</p>
Supplementary information for samples and data used in Salazar et al. (2019)
<p>Supplementary information for samples and data used in Salazar et al. (2019). <em>Gene expression changes and community turnover differentially shape the global ocean metatranscriptome</em>. https://doi.org/10.1016/j.cell.2019.10.014</p>
Supplementary Information: The Late Lutetian Thermal Maximum: first record of the deep-sea benthic foraminiferal response in the Tasman Sea (IODP Site U1508)
<div> <div> <div> <div> <div> <div> <div> <div> <p>Here, we provide supplementary information including inorganic and organic geochemistry data, magnetostratigraphy data, foraminifera data, and the age model derived from sediment samples taken from IODP Hole U1508C.</p> </div> </div> </div> </div> <p>At this IODP Hole, from the Tasman Sea, we have documented the Late Lutetian Thermal Maximum (LLTM), a brief warming event in the middle Eocene (41.52 Ma). This is the first record of deep-sea benthic foraminifera changes during the LLTM in the SW Pacific. The event coincides with negative δ13C excursions in bulk sediment (0.47‰) and benthic foraminifera (0.36‰), as well as shifts in foraminiferal species and organic geochemistry. Reduced diversity in benthic foraminiferal assemblages during the event indicates environmental stress, likely due to low oxygen conditions, as suggested by dysoxic taxa. Despite the dominance of calcareous taxa, corrosion-resistant species point to slightly CaCO3-corrosive waters without evident dissolution. We infer a shallower thermocline and increased stratification during the LLTM.</p> <div> <div> <div> <div> </div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <p> </p> </div> </div> </div>
Figure 1 in Additional information on the Giant Himalayan Horned Frog, Megophrys periosa Mahony, Kamei, Teeling & Biju, 2018 (Anura: Megophryidae)
Figure 1. Maximum Likelihood (ML) tree for the species of Megophrys based on 521 bp of mt 16s DNA.
Text-fig. 1. Geographical position of the locality in Komořany Lake - A Lost Archive For Palaeobotanical, Archaeological And Historical Information
Text-fig. 1. Geographical position of the locality
Fig. 15 in Fossil Cantharidae from the Cretaceous Burmese (Kachin) amber of the Patrick Müller collection, and taxonomic information
Fig. 15. Sanaungulus myanmaricus sp. nov. in Burmese amber. Holotype, lateral view.
Fig. 12 in Fossil Cantharidae from the Cretaceous Burmese (Kachin) amber of the Patrick Müller collection, and taxonomic information
Fig. 12. Sanaungulus kachinensis sp. nov. in Burmese amber. A–B: Holotype, lateral views.
Fig. 10 in Fossil Cantharidae from the Cretaceous Burmese (Kachin) amber of the Patrick Müller collection, and taxonomic information
Fig. 10. Sanaungulus franziskaeweissbachae sp. nov. in Burmese amber. Holotype, lateral view.
Figure 93 in Review of the genus Neomardara Hering, 1926 with descriptions of two new species and updated information on the distribution of the genus (Lepidoptera: Erebidae: Lymantriinae)
Figure 93. Distribution of the genus Neomardara.
Supporting Information for "The polarity of IMF By strongly modulates particle precipitation during high-speed streams"
<p>This data file contains start times of 485 high-speed stream events, used in the manuscript (for Geophysical Research Letters) "The polarity of IMF By strongly modulates particle precipitation during high-speed streams". </p>
Fig. 1 in Filling the Corallian gap: New information on Late Jurassic marine reptile faunas from England
Fig. 1. Palaeogeographical map of Mid-Oxfordian in the UK (from Bradshaw et al. 1992 and Coe 1995).
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.