Find research datasets worth reusing

Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025

We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de

Time series of high-frequency meteorological data at Carvins Cove Reservoir, Virginia, USA 2021-2025

This dataset consists of variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Carvins Cove Reservoir. Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.36944, -79.95778), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetically active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every minute from 2021-03-29 19:00:00 to the end of the dataset at 2025-12-31 23:59:00, except for periods of maintenance. We applied extensive quality assurance/quality control protocols to the observations, as described in the methods. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.

openCC (other)Oct 2024View details →

Sediment trap time series data for Beaverdam Reservoir and Falling Creek Reservoir in southwestern Virginia, USA 2018 through 2023

Sediment traps were deployed to assess the mass and composition (lithium, sodium, magnesium, aluminum, potassium, calcium, iron, manganese, copper, strontium, barium, total organic carbon, and total nitrogen) of settling particulates in the water column of two drinking water reservoirs—Beaverdam Reservoir and Falling Creek Reservoir, both located in Vinton, Virginia, USA. Sediment traps were deployed at two depths in each reservoir to capture both epilimnetic and hypolimnetic (total) sediment flux. The particulates were collected from the traps approximately fortnightly from April to December from 2018 to 2023, then filtered, dried, and analyzed for lithium, sodium, magnesium, aluminum, potassium, calcium, iron, manganese, copper, strontium, and barium (2018 to 2023) and total organic carbon and total nitrogen (2018 to 2022, due to instrument repairs). Beaverdam and Falling Creek are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia. The sediment trap dataset consists of logs detailing the sample filtering process, the mass of dried particulates from each filter, and the raw concentration data for lithium (Li), sodium (Na), magnesium (Mg), aluminum (Al), potassium (K), calcium (Ca), iron (Fe), manganese (Mn), copper (Cu), strontium (Sr), barium (Ba), total organic carbon (TOC) and total nitrogen (TN). The final products are the calculated downward fluxes of solid Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba, TOC, and TN during the aforementioned deployment periods.

Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025

We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.

Time series of high-frequency meteorological data at Falling Creek Reservoir, Virginia, USA 2015-2025

This dataset consists of meteorological variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Falling Creek Reservoir (37.3025, -79.83667). Falling Creek Reservoir (Vinton, Virginia, USA), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetic active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every 5 minutes from 2015-07-07 15:45:00 to 2015-07-13 11:28:00 (YYYY-MM-DD hh:mm:ss) and every minute thereafter to the end of the dataset at 2025-12-31 23:59:00. We applied substantial Quality Assurance/Quality Control (QA/QC) protocols to the raw observations, as described in the methods. The dataset is accompanied by a sensor maintenance log and QA/QC analysis scripts.

Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025

We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d

openCC (other)Dec 2022View details →

LAGOS-NE v.1.054.1 - Lake water quality time series and geophysical data from a 17-state region of the United States

Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix “iws”) and HUC 4 (variable prefix “hu4”) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.

zenodo52/100

Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot

This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1].  The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1].    <h4>Dataset Structure:</h4> <ul> <li>File: <code>ForceTorqueTimeSeries.csv</code> <ul> <li>Columns: <ul> <li><code>idx (1-524)</code>: Index of the recording corresponding to the assembly of one wheel.</li> <li><code>label (true/false)</code>: Indicates whether the assembly was successful (TRUE = product is OK).</li> <li><code>meas_id (1-6)</code>: Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).</li> <li><code>force_x</code>: X-component of the force measured by the sensor mounted on the delta robot's end effector.</li> <li><code>force_y</code>: Y-component of the force.</li> <li><code>force_z</code>: Z-component of the force.</li> <li><code>torque_x</code>: X-component of the torque.</li> <li><code>torque_y</code>: Y-component of the torque.</li> <li><code>torque_z</code>: Z-component of the torque.</li> </ul> </li> </ul> </li> </ul> <h4>Additional Files:</h4> <ul> <li><code>IMG_3351.MOV</code>: A video demonstrating the assembly process for one batch of four wheels.</li> <li><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>: Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>: Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). Anomaly detection in robotic assembly process using force and torque sensors [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). Edge AI integration for anomaly detection in assembly using Delta robot [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>

opencc-by-4.0Sep 2024View details →

opencc-by-4.0Apr 2020View details →

Overview of the time series in the PALMOD 130k marine palaeoclimate data synthesis

Palaeoclimate time series in the PALMOD 130k marine palaeoclimate data synthesis v1.0.1. This table lists the site names and location, parameters including additional information as well as the source of the data and the original publications where the data were presented.

opencc-by-sa-4.0Nov 2023View details →

Energy System Time Series Suite (ESTSS) - Data Archive

<h2>Energy System Time Series Suite - Data Archive</h2>   This archive contains variously sized sets of declustered time series within the context of energy systems. These series demonstrate low discrepancy and high heterogeneity in feature space, resulting in a roughly uniform distribution within this space. For detailed information, please refer to the corresponding GitHub project: <a href="https://github.com/s-guenther/estss/">https://github.com/s-guenther/estss/</a> For associated research, see <a href="https://doi.org/10.1186/s42162-024-00304-8">https://doi.org/10.1186/s42162-024-00304-8</a> Data is provided in .csv format. The GitHub project includes a Python function to load this data as a dictionary of pandas data frames. Should you utilize this data, kindly also cite the associated research paper. For any queries, please feel free to reach out to us through GitHub or the contact details provided at the end of this readme file.   <h3>Folder Content</h3> <ul> <li>`ts_*.csv`: Contains declustered load profile time series in tabular format. <ul> <li>Size: `(n+1) x (m+1)`, with `n` representing time steps (1000 per series) and `m` the number of series.</li> <li>Includes a header row and index column. Headers indicate series id, and the index column numbers each time step, starting from `0`.</li> <li>The first half of the series `(m/2)` consistently display a constant sign (negative). They are sequentially numbered from 0.</li> <li>The second half `(m/2)` display varying signs. Numbering starts from `1,000,000`.</li> </ul> </li> <li>`features_*.csv`: Tabulates features corresponding to the time series. <ul> <li>Size: `(m+1) x (f+1)`, where `m` is the number of time series and `f` is the number of features</li> <li>Includes a header row and index column. Indexes represent time series id (matching `ts_*.csv` headers), and headers name the features.</li> </ul> </li> <li>`norm_space_*.csv`: Shows feature vectors in normalized feature space where time series are declustered. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x (g+1)`, where `m` is the number of timer series and `g` is the number of selected features space features. (a subset of `f` from `features_*.csv`).</li> <li>Format matches `features_*.csv`.</li> </ul> </li> <li>`info_*.csv`: Maps declustered datasets to the manifolded dataset. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x 2`, with `m` as series count. Columns contain manifolded set time series ids.</li> <li>Includes an index column and a header. The index holds the remapped id of declustered series. Header `0` is non-significant.</li> </ul> </li> </ul> Each `ts_*.csv`, `features_*.csv`, `norm_space_*.csv`, and `info_*.csv` file comes in four versions to accommodate various set sizes: <ul> <li>`*_4096.csv`</li> <li>`*_1024.csv`</li> <li>`*_256.csv`</li> <li>`*_64.csv`</li> </ul> These represent sets with 4096, 1024, 256, and 64 time series, respectively,offering different densities in feature space population. The objective is to balance computational load and resolution for individual research needs.   <h3>Contact</h3> ESTSS - Energy System Time Series Suite Copyright (C) 2023 Sebastian Günther sebastian.guenther@ifes.uni-hannover.de Leibniz Universität Hannover Institut für Elektrische Energiesysteme Fachgebiet für Elektrische Energiespeichersysteme Leibniz University Hannover Institute of Electric Power Systems Electric Energy Storage Systems Section <a href="https://www.ifes.uni-hannover.de/ees.html">https://www.ifes.uni-hannover.de/ees.html</a>

opencc-by-4.0Oct 2021View details →

Synthetic time series data generation for edge analytics

In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data

opencc-by-4.0Aug 2021View details →

CAMISIM hybrid time series data

CAMISIM was used to simulate Illumina and Nanopore reads for a time series based on the genome sources from the “CAMI II challenge toy mouse gut dataset” (Meyer et al., 2021), containing 791 genomes. For this, the most recent development version of CAMISIM at the time of preparing this data was used (available at <a href="https://doi.org/10.5281/zenodo.5137751">https://doi.org/10.5281/zenodo.5137751</a>). Two groups of samples were generated by using different CAMISIM seeds, each comprising a time series of four samples. The sample sheet file (samplesheet.CAMISIM_hybrid.csv) can be used as direct input for the nf-core/mag pipeline, e.g. with the command: > nextflow run nf-core/mag -r 2.1.0 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input https://zenodo.org/record/5155395/files/samplesheet.CAMISIM_hybrid.csv --coassemble_group Note, in case of download problems, restarting the pipeline run with `-resume` or downloading the files beforehand and adjusting the paths in the sample sheet file should help. See <a href="https://nf-co.re/mag">https://nf-co.re/mag</a> for a comprehensive usage documentation.

opencc-by-4.0Jun 2023View details →

Multivariate Time Series data of Fatigued and Non-Fatigued Running from Inertial Measurement Units

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. 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 are segmented strides from the two 400m runs of each of the 19 participants. The labels on the data represent the participant number and whether it was a fatigued stride ('F') or a not fatigued stride ('NF'). The data used from the sensors includes data from the accelerometer in three directions (X, Y, Z) and the gyroscope in three directions (X, Y, Z). The direction of each of the axis is relative to the sensor. Two extra signals, magnitude acceleration and magnitude gyroscope were derived from the component signals and included in the analysis.Kindly cite one of the following papers when using this data: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/BSN58485.2023.10331612B. 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>

opencc-by-4.0Jun 2023View details →

Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"

This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude  and the sun's radio flux at 10.7 cm <F10.7>. measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.

opencc-by-4.0Dec 2019View details →

Magneto-telluric data from the Los Humeros geothermal field in Mexico: time series and edi-files

The data are from the Los Humeros geothermal field in Mexico. The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series. A file containing the location of the soundings and calibration files are also in the dataset. The Metronix equipment was used to acquire the data. The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.

opencc-by-4.0Dec 2019View details →

Magneto-telluric data from the Acoculco area in Mexico: time series and edi-files

The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series. The Metronix equipment was used to acquire the data. The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.

opencc-by-4.0Nov 2021View details →

Long time-series ecological niche modelling using archaeological settlement data.

CR_settlement_niche_[N]_[Yr]_[BC/AD].tif Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement. N - chronological ordering Yr, BC/AD - calendar years BC or AD   CR_settlement_niche_combined.tif All models combined by averaging.   CR_settlement_archeo.zip Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields: Site_Type: Cemetery or Settlement Archeo_Dat: Archaeological dating (culture or period) Source: Source dataset (AMCR or LONGWOOD) AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/. LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016).  Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554. Yrs_BP_Avg: Average dating in calendar years BP (based on the archaeological dating) Yrs_BP_Unc: Temporal uncertainty of the dating (half of the culture or period's duration) Loc_Accur: Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)

opencc-by-4.0Nov 2021View details →

GeoERA RESOURCE CHAKA data set which contains time series of precipitation and discharge of springs in the CHAKA pilot areas (D5.5)

Dataset which contains time series of precipitation and discharge of springs in the pilot areas of the CHAKA work package of the GeoERA RESOURCE project. The file contains precipitation and spring discharge data of 16 pilot areas in the Karst & Chalk work package. A description of the application of the dataset for the characterisation of the typology of karst systems in given in the D5.3 deliverable of GeoERA RESOURCE of which the pdf is provided. Further information about the CHAKA results can be assessed though the webservices of the European Geological Data Infrastructure (EGDI).

opencc-by-4.0Nov 2021View details →

Data for time series tutorial

These are sample data files to be used in the time series tutorial found here: <a href="https://github.com/abigailStev/timeseries-tutorial">https://github.com/abigailStev/timeseries-tutorial </a> They are public datasets from the NICER X-ray Timing Instrument of a black hole, MAXI J1535-571, and a neutron star, Swift J0243.6+6124. There are also Good Time Intervals I created for each of the photon event lists.