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Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </p>
Meter-Scale Magma-Water Interaction Experiments
<p>These are video and other sensor data of experiments in which "magma" — that is: volcanic rock, re-melted at ca. 1300°C — interacts with liquid water. The experiments aim to better understand the escalation behavior of the processes involved when magma comes into contact with liquid water.</p> <p>The dataset will grow over time as data of new experiments is added.</p> <p><strong>Changes</strong></p> <ul> <li>Version 1.0: Add the <code>pr06</code> experiment.</li> <li>Version 0.11: Add the <code>pr05</code> experiment.</li> <li>Version 0.10: Add the <code>ir16</code> experiment.</li> <li>Version 0.9: Add the <code>ir15</code> experiment.</li> <li>Version 0.8: Add the <code>ir14</code> experiment.</li> <li>Version 0.7: Add the <code>ir13</code> experiment.</li> <li>Version 0.6: Add the <code>ir12</code> experiment.</li> <li>Version 0.5: Add the <code>ir07</code> experiment.</li> <li>Version 0.4: Add the <code>ir06</code> experiment.</li> <li>Version 0.3: Add the <code>ir05</code> experiment.</li> <li>Version 0.2: Add the <code>ir04</code> experiment.</li> <li>Version 0.1: Start with experiment <code>ir03</code>.</li> </ul>
PsPM-FSS6B: SCR and PSR measurements in a delay fear conditioning task with somatosensory CS and electrical US
<p>This dataset includes skin conductance response (SCR) and pupil size response (PSR) measurements. Also included are CS and US information, keypress responses, keypress response times and key correctness for each of 18 healthy unmedicated participants (10 males and 8 females aged 25.7+/-5.0 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Simple and complex CS are delivered to the intermediate phalanges of the index and middle fingers of the non-dominant hand. Simple stimuli are stimulations to either index or middle finger, complex stimuli are stimulations of different temporal structure to both index and middle fingers. CS intensity is set to a perceivable but not unpleasant level. US is a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
Dataset for a machine learning tool to improve lymph node staging with FDG-PET/CT
<p>This upload provides Open Data associated with the publication "A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [<sup>18</sup>F]FDG-PET/CT parameters" by Rogasch JMM <em>et al.</em> (2022).</p> <p>The upload contains the anonymized dataset with 10 features necessary for the final GBM model that was presented in the publication. However, the original full dataset with 40 features was excluded from this Open Data repository because it may not comply with strict rules of data anonymization. The full dataset can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request.</p> <p>Besides the dataset, this upload provides the original python and R scripts that were used as well as their output.</p> <p>A description of all files can be found in "content_description_2022_11_19.txt".</p> <p>A user-friendly web tool that implements the final machine learning model can be found here: <a href="https://baumgagl.github.io/PET_LN_calculator/">PET_LN_calculator</a> </p>
Alpha-2 Adrenoreceptor Antagonist Yohimbine Potentiates Consolidation of Conditioned Fear (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Panitz, C., Skoluda, N., Nater, U. M., Pizzagalli, D. A., Hermann, C., & Mueller, E. M. (2022). Alpha-2 adrenoreceptor antagonist yohimbine potentiates consolidation of conditioned fear. <em>International Journal of Neuropsychopharmacology</em>, 25(9), 759–773.</strong></p> <p><em>Background:</em> Hyperconsolidation of aversive associations and poor extinction learning have been hypothesized to be crucial in the acquisition of pathological fear. Previous animal and human research points to the potential role of the catecholaminergic system, particularly noradrenaline and dopamine, in acquiring emotional memories. Here, we investigated in a between-participants design with 3 groups whether the noradrenergic alpha-2 adrenoreceptor antagonist yohimbine and the dopaminergic D2-receptor antagonist sulpiride modulate long-term fear conditioning and extinction in humans.<br><em>Methods:</em> Fifty-five healthy male students were recruited. The final sample consisted of n = 51 participants who were explicitly aware of the contingencies between conditioned stimuli (CS) and unconditioned stimuli after fear acquisition. The participants were then randomly assigned to 1 of the 3 groups and received either yohimbine (10 mg, n = 17), sulpiride (200 mg, n = 16), or placebo (n = 18) between fear acquisition and extinction. Recall of conditioned (non-extinguished CS+ vs CS−) and extinguished fear (extinguished CS+ vs CS−) was assessed 1 day later, and a 64-channel electroencephalogram was recorded.<br><em>Results:</em> The yohimbine group showed increased salivary alpha-amylase activity, confirming a successful manipulation of central noradrenergic release. Elevated fear-conditioned bradycardia and larger differential amplitudes of the N170 and late positive potential components in the event-related brain potential indicated that yohimbine treatment (compared with a placebo and sulpiride) enhanced fear recall during day 2.<br><em>Conclusions:</em> These results suggest that yohimbine potentiates cardiac and central electrophysiological signatures of fear memory consolidation. They thereby elucidate the key role of noradrenaline in strengthening the consolidation of conditioned fear associations, which may be a key mechanism in the etiology of fear-related disorders.</p>
Data from the behavioural and Magnetic resonance imaging of the Ts66Yah and Ts65Dn male model of Down syndrome
<p>Please find enclosed the behavioural and Magnetic Resonnance Imaging (MRI) variables used for comparing the Ts66Yah DS models with the parental line Ts65Dn. The raw data are found as two CVS files</p> <p>- Behavioural phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>- MRI phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>while the processed data used for the GDAPHEN analysis (https://github.com/YaH44/GDAPHEN/releases/tag/Public) are available as Excel docs.</p> <p> </p> <p>The processing has been done with a low level of imputation for missing data detailed in the Formating_decision_phenoParameters_Ts65Dn_Ts66Yah. ...</p>
UK Hansard 1935-2014
<p><strong>UK Hansard 1935-2014</strong></p> <p>The "uk_hansard_1935_2014_BvW_2022.tsv" is a metadata enriched version of the Hansard corpus. It is one of the outputs from <a href="https://www.arts.kuleuven.be/mosa/english/staff/00101799">Betto van Waarden</a>'s Marie Skłodowska-Curie project "Presenting Parliament: Parliamentarians’ visions of the communication and role of parliament within the mediated democracies of Britain, Belgium and the Netherlands, 1844-1995 ".</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 897761.</p> <p>The project has been executed in collaboration with <a href="https://www.medarbetarwebben.lu.se/lucat/user/db5363013678aa825cd50841818af81f">Mathias Johansson</a> from the <a href="https://projekt.ht.lu.se/digitalhistory/">DigitalHistory@Lund</a> platform, Lund University</p> <p><em>Base corpora</em></p> <p>The attached file is an agglomeration of two preexisting versions of the UK Hansard corpus: <a href="https://www.politicalmashup.nl/">Political Mashup</a>, provided to us by <a href="https://www.turing.ac.uk/people/researchers/kaspar-beelen">Kaspar Beelen</a> and a .tsv file we received from <a href="https://munkschool.utoronto.ca/person/ludovic-rheault">Ludovic Rheault</a> which itself is based on the Political Mashup corpus.</p> <p>This corpus is based on the Rheault version and is reproduced with his written permission. The original corpus contained the following columns:</p> <ul> <li>cabinet</li> <li>function</li> <li>parliament</li> <li>party</li> <li>party_in_power</li> <li>speaker_id</li> <li>speech_id</li> <li>speech_text</li> <li>topic</li> <li>year</li> </ul> <p>To which we have added the following columns:</p> <ul> <li>date</li> <li>times_in_house</li> <li>seniority</li> <li>district</li> <li>district_class</li> </ul> <p>Changed the 'year' column to 'date' as it contained the ISO8061-formatted date, leaving only the year in the 'year' column</p> <p><em>Career</em></p> <p>In the Political Mashup data there is information about which offices each speaker has held and using this data we have calculated how many terms a speaker has held office at the time of a speech <em>times_in_house</em>. From this we derived a column we call <em>seniority</em>: speakers that have been in office at most one time before the current parliament/round is classified as a junior - everyone else as a senior.</p> <p><em>Districts</em></p> <p>By cross referencing the speaker_ids against the Political Mashup we have extracted which district each speaker was representing. We have then mapped these districts against a classification of UK districts (<a href="https://commonslibrary.parliament.uk/research-briefings/cbp-8322/">Baker, 2018</a>, accessed on 2021-07-12) that uses six classes which we reduced to three classes for simplicity's sake:</p> <table> <thead> <tr> <th>Original</th> <th>Reduced</th> </tr> </thead> <tbody> <tr> <td>Core City <p>Other City</p> </td> <td>city</td> </tr> <tr> <td>Large Town <p>Medium Town</p> </td> <td>town</td> </tr> <tr> <td>Small Town <p>Village or Smaller</p> </td> <td>village</td> </tr> </tbody> </table> <p>Mapping districts to one of the three classes was mostly done automatically by matching district names against the list, and districts that have split or merged over time were processed manually. Still, not all districts were resolved satisfactorily leaving 43,541 speeches without a district classification resulting in a coverage of 98.7%.</p> <table> <thead> <tr> <th>class</th> <th>count</th> </tr> </thead> <tbody> <tr> <td>town</td> <td>1,253,330</td> </tr> <tr> <td>city</td> <td>1,184,605</td> </tr> <tr> <td>village</td> <td>908,606</td> </tr> <tr> <td>N/A</td> <td>43,541</td> </tr> </tbody> </table>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis
<p>Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression.</p> <p>The data was saved with numpy 1.21 with <a href="https://numpy.org/doc/1.21/reference/generated/numpy.lib.format.html#format-version-1-0">NumPy format version 1.0</a> as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data.</p> <p>In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with <a href="https://imagej.nih.gov/ij/">ImageJ</a>.</p> <p> </p> <p>A <a href="https://github.com/Kitware/MetaIO">METAImage</a> header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,</p>
pastclim_Beyer2020
<p>Dataset for the R package pastclim, covering the data from Beyer et al. 2020. This is a version of the dataset with ice sheets and internal seas removed.</p> <p>website of the package: https://evolecolgroup.github.io/pastclim/index.html</p> <p> </p>
Umfragedaten Forschungsdatenmanagement 2022 der BUA-Einrichtungen
<p>Das 2020-22 von der Berlin University Alliance (BUA) geförderte Projekt „Concept Development for Collaborative Research Data Management Services“ hat sich die Konzeptentwicklung zum nachhaltigen Aufbau von Kompetenz und Expertise zum Thema FDM für Forschende und Multiplikator*innen sowie die Stärkung von Services rund um das FDM innerhalb der BUA zum Ziel gesetzt, um bereits bestehende Ressourcen bedarfsgetrieben bestmöglich nutzbar zu machen, Parallelentwicklungen zu vermeiden und Synergieeffekte zu ermöglichen. Hierfür wurde im Zeitraum 11/2021 bis 01/2022 an den vier BUA-Einrichtungen (Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin, Charité – Universitätsmedizin Berlin) eine Bestands- und Bedarfserhebung zum Umgang mit Forschungsdaten durchgeführt, deren Ergebnisse als Basis für die Entwicklung bedarfsorientierter standortspezifischer und standortübergreifender Beratungs-, Schulungs-, Kommunikations- und technischer Serviceleistungen dienen. Ziel der Befragungen war es, zu ermitteln, welche Services</p> <ol> <li> <p>aktuell an den jeweiligen Standorten bekannt sind und genutzt werden</p> </li> <li> <p>an allen Standorten gewünscht, aber noch nicht angeboten werden</p> </li> <li> <p>im Verbund als fruchtbar erachtet werden, um qualitätsvolle Forschung auch institutionsübergreifend zu ermöglichen.</p> </li> </ol>
functional MRI study on the language stress perception in a foreign language
<p>fMRI dataset of 91 participants during a linguistic task about language stress perception in a foreign language.</p> <p>Participants listened to pairs of words in a foreign language (Spanish) and had to indicate if the words were the same or different. The different pairs differed either by the stress pattern, or by the final vowel.</p> <p>This dataset was divided into two groups: 51 participants with French as native language and 40 with Swiss-German as native language. None of the participants had knowledge of Spanish.</p> <p>This repository respects the BIDS standard (<a href="https://bids.neuroimaging.io/">https://bids.neuroimaging.io/</a>), including all the raw data (func, fmap, anat) and metadata in order to reproduce the processing.</p> <p>These data have been used in two papers:</p> <p>S. Schwab, M. Mouthon, L.B. Jost, J. Salvadori, I. Yakoub, E. Ferreira da Silva, N. Giroud, B. Perriard and J.M. Annoni, Neural correlates of lexical stress processing in a foreign free-stress language; Brain and Behavior (2023)</p> <p>L. Rogenmoser, M. Mouthon, F. Etter, J. Kamber, J.M. Annoni and S. Schwab; The processing of stress in a foreign language modulates functional antagonism between default mode and attention network regions, (submitted)</p>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</p>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Model Reproducibility Study on Left Atrial Fibres
<p>This dataset contains 100 models of the left atrium. Models come from 50 distinct patients, divided amongst 5 users to assess for inter- and intra-operator variability. The split used was 30 pairs (60 models) for inter-operator variability and 20 pairs (40 models) for intra operator variability. Models were created with a specific version of the software CemrgApp (cemrgapp.com), in which users processed a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR).</p> <p>Two types of simulations were run on each of the 100 processed cases: baseline pacing to calculate local activation time (LAT) maps and atrial fibrillation simulations for which phase singularity (PS) maps were calculated. The openCARP simulator (Plank et al., 2021) was used to run the simulations, using the Courtemance human atrial model with AF electrical remodelling.</p> <p>This dataset contains the labelled surface meshes, output to the CemrgApp software, which in turn are utilised as inputs for the electrophisiological simulations.</p> <p>The dataset is split into 100 folders labelled M1 to M100. Included in file `Cases_and_Users_Paths.csv` is the pairs for each of the comparisons, whether inter- or intra-observer variability.</p>
PsPM-LI: SCR, ECG, PSR and respiration measurements in a delay fear conditioning task with auditory CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response(SCR), electrocardiogram (ECG) and respiration measurements for each of 20 healthy unmedicated participants (8 males and 12 females aged 22.8+/-3.3 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. (One additional participant in the initial sample in Korn et al. (2017) - but who did not finish the experiment and was not included into the analysis - is not contained in this dataset.) The acquisition data is separated into two sessions which were recorded consecutively with a break of approximately 5 min. CS consist of two sine tones with constant frequency (220 Hz or 440 Hz, 50-ms onset and offset ramp) and last for 6.5 s. US is a 0.5 s train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA betwen the CS and US is 6 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
Unmanned Aerial Vehicles Dataset
<p><strong>Unmanned Aerial Vehicles Dataset:</strong></p> <p>The Unmanned Aerial Vehicle (UAV) Image Dataset consists of a collection of images containing UAVs, along with object annotations for the UAVs found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for UAVs. The dataset is intended for use in research and development of UAV-related applications, such as autonomous flight, collision avoidance and rogue drone tracking and following. The dataset consists of the following images and detection objects (Drone):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Drone</td> </tr> <tr> <td>Training</td> <td>768</td> <td>818</td> </tr> <tr> <td>Validation</td> <td>384</td> <td>402</td> </tr> <tr> <td>Testing</td> <td>383</td> <td>400</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Rafael Makrigiorgis, Nicolas Souli, & Panayiotis Kolios. (2022). Unmanned Aerial Vehicles Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7477569</p> </blockquote>
Selecting for infectivity across metapopulations can increase virulence in the social microbe Bacillus thuringiensis:data set.
<p>Passage experiments that sequentially infect hosts with parasites have long been used to manipulate virulence. However, for many invertebrate pathogens passage has been applied naively without a full theoretical understanding of how best to select for increased virulence and this has led to very mixed results. Understanding the evolution of virulence is complex because selection on parasites occurs across multiple spatial scales with potentially different conflicts operating on parasites with different life-histories. For example, in social microbes, strong selection on replication rate within hosts can lead to cheating and loss of virulence, because investment in public goods virulence reduces replication rate. </p> <p>In this study<em> </em>we tested how varying mutation supply and selection for infectivity or pathogen yield (population size in hosts) affected evolution of virulence against resistant hosts in the specialist insect pathogen <em>Bacillus thuringiensis</em>, aiming to optimize methods for strain improvement against a difficult to kill insect target. We show that selection for infectivity using competition between sub-populations in a metapopulation prevents social cheating, acts to retain key virulence plasmids and facilitates increased virulence. Increased virulence was associated with reduced efficiency of sporulation, and possible loss of function in putative regulatory genes but not with altered expression of the primary virulence factors. Selection in a metapopulation provides a broadly applicable tool for improving the efficacy of biocontrol agents. Moreover, a structured host population can facilitate artificial selection on infectivity, while selection on life history traits such as faster replication or larger population sizes can reduce virulence in social microbes.</p>
Six years of demography data for 11 reef coral species
<p>Scleractinian corals are colonial animals with a range of life history strategies that make up diverse species assemblages that contribute to coral reef growth. We tagged and tracked approximately 30 colonies from each of 11 species for six years (2009-2015) in order to measure their vital rates and competitive interactions on the reef crest at Trimodal Reef, Lizard Island, Australia. Pairs of species were chosen from five growth forms (massive [<em>Goniastrea pectinata </em>and <em>G. retiformis</em>], digitate [<em>Acropora humilis</em> and <em>A. cf. digitifera</em>], corymbose [<em>A. millepora</em> and <em>A. nasuta</em>], tabular [<em>A. cytherea</em> and <em>A. hyacinthus</em>] and arborescent [<em>A. robusta</em> and <em>A. intermedia</em>]) where one species of the pair was locally rare and the other abundant. (An extra corymbose species, <em>A. spathulata</em> was included when it became apparent that <em>A. millepora</em> was too rare to work with on the reef crest, making the 11 species in total.) The tagged colonies were visited each year in the weeks prior to mass spawning. During visits, photographs were taken by two or more observers from directly above and on the horizontal plane with a scale plate to track planar area. Dead or missing colonies were recorded and new colonies tagged in order to maintain approximately 30 colonies per species throughout the six years of the study. In addition to tracking tagged corals, 30 fragments were collected from neighboring untagged colonies of each species for counting numbers of eggs per polyp (fecundity); and fragments of untagged colonies were brought into the laboratory where spawned eggs were collected for size and energy measurements. We also conducted surveys at the study site to generate size structure data for each species in several of the years. Each tagged colony photograph was digitized by at least two people. Therefore, we could examine sources of error in planar area for both photographers and outliners. Competitive interactions were recorded for a subset of species by measuring the margins of tagged colony outlines interacting with neighboring corals. The study was abruptly ended by Tropical Cyclone Nathan that killed all but nine of the over 300 tagged colonies in early 2015. Nonetheless, these data will be of use to other researchers interested in coral demography and coexistence, functional ecology, and parametrizing population, community and ecosystem models.</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.