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TV Series - Networks of characters
<p><strong>Description. </strong>This repository contains three sets of files related to the social networks of characters in the following episodes of 3 TV series:</p> <ul> <li><em>Breaking Bad</em> (BB): S01--S03;</li> <li><em>Game of Thrones</em> (GoT): S01--05;</li> <li><em>House of Cards</em> (HoC): S01--S02.</li> </ul> <p>The three separate Graphml files (`.graphml` extension) contain the static, cumulative, conversational networks at the end of the set of episodes considered. Three images (`.jpg` extension) of the resulting static, cumulative, conversational networks at the end of the first two seasons are also provided.<br><br>The compressed archives (`.tgz` extension) provide snapshots of the conversational network of characters (Graphml format) in every scene. These files come in three flavors:</p> <ol> <li><em>Narrative smoothing</em> based networks (denoted `ns`).</li> <li><em>Time-slice</em> based networks, where all interactions are agglomerated every <em>10 scenes </em>(denoted `'ts10`).</li> <li><em>Time-slice </em>based networks, where all interactions are agglomerated every <em>40 scenes </em>(denoted `ts40`).</li> </ol> <p>The video files (`.mp4` extension) contain short animations of the dynamic networks of characters as they evolve over the whole set of episodes considered:</p> <ul> <li>The nodes are represented by the names of the corresponding characters and the distance between two character names is inversely proportional to the weight of the corresponding edge: the closer they are at some point of the story, the more they interact then.</li> <li>The size of each character name is proportional to the local strength of the corresponding character at the moment considered.</li> <li>The color of each character name corresponds to his community at any moment.</li> </ul> <p>Each snapshot in these animations is based on our <em>narrative smoothing</em> approach.</p> <p>This dataset was used in the following articles:</p> <ol> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Narrative smoothing: dynamic conversational network for the analysis of TV Series plots,” in <em>2nd International Workshop on Dynamics in Networks (DyNo/ASONAM)</em>, 2016, pp. 1111–1118. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01276708">hal-01276708</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2016.7752379">10.1109/ASONAM.2016.7752379</a></li> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction de réseaux dynamiques conversationnels par lissage narratif,” in <em>7ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques</em>, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01385215">hal-01385215</a>⟩</li> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction and analysis of dynamic conversational networks from TV series,” in <em>Social Network Based Big Data Analysis and Applications</em>, Springer, 2018, pp. 55–84. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01543938">hal-01543938</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-78196-9_3">10.1007/978-3-319-78196-9_3</a></li> </ol> <p><strong>Citation. </strong>If you use this dataset, please cite the article [1]</p> <p><br><code>@InProceedings{Bost2016,</code><br><code> author = {Bost, Xavier and Labatut, Vincent and Gueye, Serigne and Linarès, Georges},</code><br><code> title = {Narrative smoothing: dynamic conversational network for the analysis of {TV} Series plots},</code><br><code> booktitle = {2nd ASONAM International Workshop on Dynamics in Networks},</code><br><code> year = {2016},</code><br><code> pages = {1111-1118},</code><br><code> address = {San Francisco, US},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ASONAM.2016.7752379},</code><br><code>}</code></p>
Serial Speakers: a Dataset of TV Series
<p><strong>Description. </strong>This dataset consists of 3 TV series with manual annotations:</p> <ol> <li><em>Breaking Bad</em>: S01--S05 (file 'bb.json');</li> <li><em>Game of Thrones</em>: S01--08 (file 'got.json');</li> <li><em>House of Cards</em>: S01--S02 (file 'hoc.json').</li> </ol> <p>All three files are in `.json` format and contain TV Series annotated data.</p> <p>Each TV Series is defined by its name, A TV Series contains seasons, defined by their ids. Every season is made of episodes, defined by their ids, titles, duration and fps. Each episode contains two basic kinds of data: scenes and speech segments. Scenes are defined by starting points and are made of shots (Seasons 1 only).<br>A shot is defined by:</p> <ul> <li>Starting and ending positions.</li> <li>Recurring shot ids.</li> </ul> <p>The speech segments are defined by their:</p> <ul> <li>Starting and ending points.</li> <li>Textual content (here encrypted for copyright reasons).</li> <li>Speaker.</li> <li>Possible interlocutors (for the following episodes only: bb: S01E04, S01E06, S02E03, S02E04; got: S01E03, S01E07, S01E08; hoc: S01E01, S01E07, S01E11).</li> </ul> <p>All timestamps are expressed in seconds and are valid for the video files extracted from the commercial DVDs (PAL 25 FPS), with recaps (unannotated) included at the beginning of the <em>House of Cards</em> episodes.</p> <p>A comprehensive description of the dataset can be found in the following paper:</p> <ul> <li>X. Bost, V. Labatut, and G. Linarès, “Serial Speakers: a Dataset of TV Series,” in <em>12th Language Resources and Evaluation Conference</em>, Marseille, FR, 2020, pp. 4249–4257. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02477736">hal-02477736</a>⟩</li> </ul> <p><strong>Source code.</strong> If you are interested in the textual content of the dataset, please consider using our text recovering tool on GitHub: <a href="https://github.com/bostxavier/Serial-Speakers">https://github.com/bostxavier/Serial-Speakers</a></p> <p><strong>Citation. </strong>If you use these data or source code, please cite the above article.</p> <p><br><code>@InProceedings{Bost2020,</code><br><code> author = {Bost, Xavier and Labatut, Vincent and Linarès, Georges},</code><br><code> title = {Serial Speakers: a Dataset of {TV} Series},</code><br><code> booktitle = {12\textsuperscript{th} Language Resources and Evaluation Conference},</code><br><code> year = {2020},</code><br><code> pages = {4249-4257},</code><br><code> address = {Marseille, FR},</code><br><code> url= {http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.524.pdf},</code><br><code>}</code></p>
Tilt-series 4DSTEM dataset of DNA origami with gold nanoparticles
<p>Raw TEM data for the manuscript "Three-dimensional Electron Ptychography of Organic-inorganic Hybrid Nanostructures".</p>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
ERA-NUTS: meteorological time-series based on C3S ERA5 for European regions (1980-2021)
<p><strong># ERA-NUTS (1980-2021)</strong></p> <p>This dataset contains a set of time-series of meteorological variables based on <a href="https://climate.copernicus.eu/climate-reanalysis">Copernicus Climate Change Service (C3S) ERA5 reanalysis</a>. The data files can be downloaded from here while notebooks and other files can be found on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</p> <p>This data has been generated with the aim of providing hourly time-series of the <strong>meteorological variables</strong> commonly used for power system modelling and, more in general, studies on energy systems.</p> <p>An example of the analysis that can be performed with ERA-NUTS is shown <a href="https://youtu.be/zVeF8Dv6jlE">in this video</a>.</p> <p><strong>Important</strong>: <em>this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us <a href="mailto:matteo.de-felice@ec.europa.eu">sending an email</a> or opening an Issue in the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</em></p> <p><strong>## Data</strong><br> The time-series have hourly/daily/monthly frequency and are aggregated following the <a href="https://ec.europa.eu/eurostat/web/nuts/background">NUTS 2016 classification</a>. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).</p> <p>This dataset contains NUTS0/1/2 time-series for the following variables obtained from the <strong>ERA5 reanalysis data</strong> (in brackets the name of the variable on the Copernicus Data Store and its unit measure):</p> <p> - <strong>t2m</strong>: 2-meter temperature (`2m_temperature`, Celsius degrees)<br> - <strong>ssrd</strong>: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)<br> - <strong>ssrdc</strong>: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)<br> - <strong>ro</strong>: Runoff (`runoff`, millimeters)<br> - <strong>sd</strong>: Snow depth (`sd`, meters)<br> <br> There are also a set of derived variables:<br> - <strong>ws10</strong>: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)<br> - <strong>ws100</strong>: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)<br> - <strong>CS</strong>: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)<br> - <strong>RH</strong>: Relative Humidity (computed following Lawrence, BAMS 2005 and Alduchov & Eskridge, 1996)<br> - <strong>HDD</strong>/<strong>CDD</strong>: Heating/Cooling Degree days (derived by 2-meter temperature the <a href="https://ec.europa.eu/eurostat/cache/metadata/en/nrg_chdd_esms.htm">EUROSTAT definition</a>.</p> <p>For each variable we have <strong>367 440 hourly samples</strong> (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for <strong>34/115/309 regions</strong> (NUTS 0/1/2).<br> <br> The data is provided in two formats:</p> <p> - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files.<br> - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)<br> <br> All the CSV files are stored in a zipped file for each variable.</p> <p><strong>## Methodology</strong></p> <p>The time-series have been generated using the following workflow:</p> <p> 1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5 hourly data on single levels from 1979 to present</a> dataset<br> 2. The data is read in R with the <a href="http://www.meteo.unican.es/climate4R">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href="https://github.com/matteodefelice/panas">panas</a>. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.<br> 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R<br> 4. The NetCDF are created using `xarray` in Python 3.8.</p> <p><strong>## Example notebooks</strong></p> <p>In the folder `notebooks` on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the <a href="https://github.com/kavvkon/enlopy">enlopy</a> package.</p> <p>There are currently two notebooks:</p> <p> - <strong>exploring-ERA-NUTS</strong>: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.<br> - <strong>ERA-NUTS-explore-with-widget</strong>: explorer interactively the datasets with [<a href="https://jupyter.org/">jupyter</a>]() and <a href="https://ipywidgets.readthedocs.io/en/stable/">ipywidgets</a>.</p> <p>The notebook `exploring-ERA-NUTS` is also available rendered as HTML.<br> <br> <strong>## Additional files</strong></p> <p>In the folder `additional files`on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.</p> <p><strong>## License</strong></p> <p>This dataset is released under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0 license</a>.</p> <p><strong>## Changelog</strong></p> <p><strong>2022-04-08 </strong>Added Relative Humidity (RH)<br> <strong>2022-03-07 </strong>Added the missing month in CDD/HDD <br> <strong>2022-02-08 </strong>Updated the wind speed and temperature data due to missing months. </p> <p> </p>
MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
<p>Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution.</p> <p>Source data:<br> - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/<br> - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/</p> <p><br> The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.</p> <p>For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data".</p> <p>File naming:<br> productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif<br> example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif</p> <p>The date is Year and Day of Year.</p> <p>Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473</p> <p>Projection + EPSG code:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG: 3035)</p> <p>Spatial extent:<br> north: 72N<br> south: 30S<br> west: -52W<br> east: 49E</p> <p>Spatial resolution:<br> 1 km</p> <p>Temporal resolution:<br> 16 days</p> <p>Pixel values:<br> NDVI/EVI * 10000 (scaled to Integer; example: value 6473 = 0.6473)</p> <p>Software used:<br> GRASS GIS 8.0</p> <p>Original dataset license:<br> All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/myd13a1v006/</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
MODIS MNDWI, 8-day time series for Europe at 1 km resolution
<p>Modified Normalized Difference Water Index (MNDWI) from MODIS data for Europe at 1 km resolution.</p> <p>Source data:<br> - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v006): https://lpdaac.usgs.gov/products/mod09a1v006/</p> <p>The corresponding MODIS/Aqua product (MYD09A1 v006) could not be used due to the fact that the Aqua satellite has a number of broken detectors resulting in unreliable data for band 6 (SWIR) measurements.</p> <p>The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.</p> <p>For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 8 days. Bad quality pixels (cloud, cloud shadow, dead detector, solar zenith angle too large, etc.) have been masked using the provided quality assurance (QA) layers and appear as "no data".</p> <p>File naming:<br> productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif<br> example: MOD09A1.A2016353_mosaic_1000m_8_days_MNDWI.tif</p> <p>The date is Year and Day of Year.</p> <p>Values are MNDWI * 10000. Example: Value -5099 = -0.5099</p> <p>Projection + EPSG code:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 72N<br> south: 30S<br> west: -52W<br> east: 49E</p> <p>Spatial resolution:<br> 1 km</p> <p>Temporal resolution:<br> 8 days</p> <p>Pixel values:<br> MNDWI * 10000 (scaled to Integer; example: value -5099 = -0.5099)</p> <p>Software used:<br> GRASS GIS 8.0</p> <p>Original dataset license:<br> All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod09a1v006/</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Time series of Agulhas leakage from satellite altimetry. Update of Le Bars et al. 2014.
<p>Agulhas leakage time series updated from Le Bars et al. 2014 and Kelly et al. 2016.</p> <p>Final results are stored in:</p> <p>Trp_track_twb122_barot30W.nc,Trp_track_twb20_barot30W.nc and Trp_track_twb198_barot30W.nc</p> <p>An example of analysis and comparison with older time series is in the Python 3.9 Jupyter notebook:</p> <p>AnalyseOutputs.ipynb (AnalyseOutputs.html to simply visualise)</p> <p>The code and intermediate results are also provided. See README.txt for instructions on how to use it. It was written in 2013 with the NCAR Command Language and Python 2.? but it can still run now with later version of NCL and with Python 2.7.</p> <p>References:</p> <p>Le Bars, D., Durgadoo, J. V., Dijkstra, H. A., Biastoch, A., and De Ruijter, W. P. M.: An observed 20-year time series of Agulhas leakage, Ocean Sci., 10, 601–609, https://doi.org/10.5194/os-10-601-2014, 2014.</p> <p>Kelly, K. A., K. Drushka, L. Thompson, D. Le Bars, and E. L. McDonagh (2016), Impact of slowdown of Atlantic overturning circulation on heat and freshwater transports, Geophys. Res. Lett., 43, doi:10.1002/2016GL069789.</p>
A Review Of Metaheuristics in Fuzzy Time Series Applied To Zero Inflated Datasets
<p>The manufacturing efficiency reflects directly on the use of natural resources and leads to a higher environmental impact than needed. Efficiency of an industry can be achieved in many ways, but it always starts with demand management. However some products have erratic and irregular demand patterns as the nature of the usage varies, and this often leads to zero inflated demand datasets, said datasets are difficult to forecast due to the nature of traditional models which usually use moving averages, state of the art machine learning models can achieve good results but use too much data for training. Under this background, this paper investigates the Fuzzy Time Series models and how it evolved from its inception to present time and how the usage of metaheuristics can help with forecasting demand on a small dataset with a high count of zeros, then applies the techniques to other zero inflated dataset to verify its generalization capabilities. Finally another model is applied as comparison.</p>
Time Series data from wearable sensors to capture the onset of Fatigue in Runners
<p>The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin.<br><br>The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here consists of the raw readings from the sensors across the three phases of the run. The data is saved seperately as 'F' for Fatigued, 'NF' for Not Fatigued, and 'BeepTest' for the data collected during the fatiguing process.</p> <p>For the processed and labelled fatigue and non fatigue data, see:</p> <p>https://zenodo.org/records/7997851</p> <p>Kindly cite one of the following papers when using this data:</p> <p>B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612</p> <p>B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a></p>
DEEPICE Stories Videos series - Insight into Ice & Climate - Videos with English subtitles
<p><em>DEEPICE Stories – Insights into Ice & Climate </em>is a series of 15 videos<em> </em>created in collaboration with the 15 PhD students of the European research project <a href="https://deepice.cnrs.fr/">DEEPICE</a>. These 3-minute educational video clips give an overview of scientific research on ice cores. <a href="https://deepice.cnrs.fr">https://deepice.cnrs.fr</a></p> <p> </p> <p><u>Credits</u></p> <p>Writing & presentation: Geunwoo Lee, Hanne <span>Notø</span>, Eirini <span>Malegiannaki</span>, Piers Larkman, Miguel Angel Sanchez Moreno, Lison Soussaintjean, Florian Painer, Niklas Kappelt, Lisa Ardoin, Inès Ollivier, Romilly Harris Stuart, Fyntan Shaw, Qinggang Gao, Ailsa Chung, Daniel Gunning</p> <p>Coordination : Marie Kazeroni (LSCE-CNRS)</p> <p>Direction: Dorothée Adam-Mazard (Inuaprod) & Marie Kazeroni (LSCE-CNRS)</p> <p>Editing: Thomas d’Aram</p> <p>Motion Design: Pauline Fuchs</p> <p>Co-production: DEEPICE & Inuaprod</p>
Temperature and wind speed time series on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains weather timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000-2018. It has been generated directly from the MERRA-2 reanalysis dataset.</p> <p>This data serves as an input to the Sector-Coupled Euro-Calliope model and has been generated to match the spatial resolution of the renewable energy generation capacity factors found at https://doi.org/10.5281/zenodo.3891480.</p> <p><em>temperature.nc:</em> air temperature at 2m above ground in degrees C.</p> <p><em>tsoil5.nc</em>: soil temperature at layer 5 in degrees C.</p> <p><em>wind10m.nc</em>: wind speed at 10m above ground in m/s.</p> <p><em>grid.nc:</em> provides the latitude and longitude of each grid (a.k.a. "site") centroid. This data is also given in every other dataset, but is provided here as a lightweight alternative to align other datasets to the same grid spacing.</p> <p> </p> <p><strong>Changelog</strong></p> <p>2024-06-07:</p> <ul> <li>Moved data variables to dataset attributes where the same data was duplicated per gridcell.</li> <li>Added units to file attributes.</li> <li>Updated `tsoil5` variable name from `soil_temperture_5` to `tsoil5`. Now all timeseries data variables have names that match the filename. </li> <li>Updated `tsoil5` variable from Kelvin to degrees C.</li> <li>Updated `tsoil5` variable empty data (when gridcell is not over land) from zero to NaN.</li> <li>Added `grid.nc`.</li> <li>Removed `electricity` variable from `wind10m.nc`, leaving only wind speed as the available timeseries data in the file.</li> </ul>
Dynamical System Multivariate Time Series
<p>The Dynamical System Multivariate Time Series (DSMTS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system under fully nominal conditions (no outliers or anomalies).</p> <p>The DSMTS Dataset exhibits a set of desirable properties that make it <strong>very suitable for benchmarking Multivariate Time Series Forecasting </strong>especially for industrial processes of complex systems:</p> <ul> <li><strong>Multivariate (17 variables)</strong> including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including: <ul> <li><strong>4 Deliberate Actuations / Control Commands sent by a simulated operator / controller</strong>, for instance, commands of an operator to turn ON/OFF some equipment.</li> <li><strong>3 Environmental Stimuli / External Forces</strong> acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.</li> <li><strong>10 Telemetry Readings representing the observable states of the complex system by means of sensors</strong>, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.</li> </ul> </li> <li><strong>5 million timestamps</strong>. Sensors readings are at 1Hz sampling frequency. </li> <li><strong>Pure signal ideal for robustness-to-noise analysis.</strong> The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.</li> <li><strong>No missing data</strong>. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.</li> </ul>
CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> <br>- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a></p> <p>Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.<br>The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.</p> <p>Processing steps:<br>To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value.</p> <p>File naming:<br>Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> <br>From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.<br>NOTE: data for 2023-04 are missing, since they are not available from CLMS</p> <p>Pixel values:<br>0: Sea<br>70: Water<br>255: No water</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 27:17:30N<br>south: 14:43:30N<br>west: 17:04:30W<br>east: 04:48:00W</p> <p>Temporal extent:<br>January 2019 - December 2023 (except: April 2023)</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>Generated using European Union's Copernicus Land Monitoring Service information</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023).</p> <p>Source data:<br>- MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p><br>The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.</p> <p>For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84.</p> <p>File naming:<br><code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code><br>e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28N<br>south: 14N<br>west: 18W<br>east: 4W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
Series data for the 22PN nonlinear memory effect in the l=2, m=0 mode.
<p>Dataset associated with the preprint arXiv:2407.19017, “Waveform models for the gravitational-wave memory effect: Extreme mass-ratio limit and final memory offset” by Arwa Elhashash and David A. Nichols. It contains the 22 post-Newtonian-order series data for the l=2, m=0 spin-weighted spherical harmonic mode of the gravitational-wave memory signal from an extreme-mass ratio inspiral with nonspinning black holes.</p>
Multi-year temperature and humidity time series from ice caves on Mauna Loa, Hawaii
<p>Air temperature and humidity time series from data loggers in two lava tube caves on Mauna Loa, Hawaii 2011-2017.</p> <p> </p>
Enron Email Time-Series Network
<p>We use the <a href="https://www.kaggle.com/wcukierski/enron-email-dataset">Enron email dataset</a> to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.</p> <p>To build a graph <em>G = (V</em><em>, E</em><em>)</em>, we use email addresses as nodes <em>V</em>. Every node <em>v<sub>i</sub></em> has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge <em>e</em><em><sub><em>ij</em></sub></em> between two nodes <em>i</em> and <em>j</em> if there is at least one email exchange between the corresponding addresses.</p> <p>Column <em>'Count'</em> in <em>'edges.csv'</em> file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.</p> <p>The file <em>'nodes.csv'</em> contains a dictionary that is a compressed representation of time-series. The format of the dictionary is <em>Day->The Number Of Emails Sent By the Address During That Day.</em> The total number of days is 1448.</p> <p><em>'id-email.csv'</em> is a file containing the actual email addresses.</p>
Photovoltaic time series for European countries and different system configurations
<p>This repository comprises 38 years-long hourly time series representing the photovoltaic (PV) capacity factors in every European country (EU-28 plus Serbia, Bosnia-Herzegovina, Norway, and Switzerland). The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity (DC). 3 letter codes (ISO-3166-3) are used to identify the countries. Time series include years from 1979 to 2017.</p> <p>To obtain PV time series irradiance from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The PV model used for the conversion is described in the article linked below. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology, which is also described in the article.</p> <p>For every country, four different time series assuming alternative PV configurations, <em>i.e</em>., rooftop, optimum tilt, 2-axis tracking, and delta are provided. To obtain the PV hourly capacity factors for a country, different assumptions on the shares of the alternative configurations can be made and the weighted time series can be aggregated accordingly.</p> <p>The license for the AU REatlas photovoltaic time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a></p> <p>When using this data please make sure you include the following citation:</p> <p><em>M. Victoria and Gorm B. Andresen, Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries, Progress in Photovoltaics: Research and Applications (2019) </em><a href="https://doi.org/10.1002/pip.3126">https://doi.org/10.1002/pip.3126</a></p> <p>More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>).</p> <p>Version 2 assumes tilt angle of 60º for PV panels in delta configuration (in version 1, tilt angle in delta configuration is equal to latitude). The remaining files do not change.</p> <p>Version 3 includes one additional file corresponding to country-wise time series obtained assuming 1 axis-tracker (horizontal axis oriented North-South). In addition, small corrections of the previous time series have been implemented affecting only early hours in the day.<br> </p>
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.