Find research datasets worth reusing

openOpenApr 2018View details →

Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): snow depth sensor data, 2012-2017

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set contains three-hourly measurements of snow depth collected with acoustic sensors for winter warming and control treatment plots.

Year 2011, January to September, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) January to September 2011 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.

Year 2010, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) year 2010 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.

Year 2009, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) year 2009 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.

Year 2008, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) year 2008 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.

Year 2007, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) year 2007 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.

Year, end of 2005 thru 2006, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for end of 2005 and all of 2006 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.

Year 2011, September-December, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) September 2011 through December 2011 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2012, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2012 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2013, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2013 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2014, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2014 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2015, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2015 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

Year 2016, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2016 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

openCC (other)Jan 2018View details →

Year 2017, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2017 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

opencc-by-4.0Dec 2019View details →

Sensor data from three different fishing ships for a period of one month

The purpose of the data is to analyse data produced by fishing ships in order to reduce fuel consumption. The data has been used in WP3 of DataBio project. Data were processed and supplied by the VTT and UPV/EHU DataBio project team.

opencc-by-4.0Apr 2020View details →

Test data for Sonic Kayaks particulate matter sensor, temperature and GPS

These data sets are the first trials for adding a particulate matter sensor to the Sonic Kayak project https://fo.am/activities/kayaks/ There are three data files - gps.csv is the GPS co-ordinates, pm.csv is the particulate matter data (using a PMS7003), and temp.csv is the temperature data (two separate but identical digital thermometer sensors). Time is included in all datasets and can be used to align them. Together the data can be used to make a fine scale heat line map of particulate matter and temperature.

opencc-by-4.0May 2020View details →

Five-minute average horizontal wind velocity data combined from both sensors (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.

Dataset abstract The horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4 has been corrected for air-flow distortion. The measurements from both the port and starboad side anemometer were averaged to five-minute resolution and have been combined via vector averaging of the data. The ten meter neutral wind speed (U10N) has been estimated using ERA-5 surface heat fluxes, which were interpolated onto the ship's track, and the COARE 3.5 drag coefficient. This data set provides a continous and high-resolution record of the wind speed and direction near to the ship's location. Dataset contents <ul> <li>wind-observations-port-stbd-corrected-combined-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> Dataset license This five-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

dryad36/100

Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors

The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets. Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals. These data are intended to be re-used, and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments. The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons. The initial release includes derived data from from two seasons in which the sorghum diversity panel was evaluated. Future releases will include data from additional seasons and locations. The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering.

opencc-zeroAug 2020View details →

opencc-by-4.0Nov 2020View details →

Data Visualization of Weight Sensor and Event Detection of Aifi Store

<a href="https://www.aifi.com/">Aifi</a> Store is an autonomus store for cashier-less shopping experience which is achieved by multi modal sensing (Vision modality, weight modality and location modality). Aifi Nano store layout (Fig 1) (Image Credits: <a href="https://dl.acm.org/doi/10.1145/3360322.3361018">AIM3S</a> research paper). Overview: The store is organized in the gondola's and each gondola has shelfs that holds the products and each shelf has weight sensor plates. These weight sensor plates data is used to find the event trigger (pick up, put down or no event) from which we can find the weight of the product picked. Gondola is similar to vertical fixture consisting of horizontal shelfs in any normal store and in this case there are 5 to 6 shelfs in a Gondola. Every shelf again is composed of weight sensing plates, weight sensing modalities, there are around 12 plates on each shelf. Every plate has a sampling rate of **60Hz**, so there are 60 samples collected every second from each plate The pick up event on the plate can be observed and marked when the weight sensor reading decreases with time and increases with time when the put down event happens. Event Detection: The event is said to be detected if the moving variance calculated from the raw weight sensor reading exceeds a set threshold of (10000gm^2 or 0.01kg^2) over the sliding window length of 0.5 seconds, which is half of the sampling rate of sensors (i.e 1 second). There are 3 types of events: <ol> <li>Pick Up Event (Fig 2)= Object being taken from the particular gondola and shelf from the customer</li> <li>Put Down Event (Fig 3)= Object being placed back from the customer on that particular gondola and shelf</li> <li>No Event = (Fig 4)No object being picked up from that shelf</li> </ol> NOTE: <ol> <li>1.The python script must be in the same folder as of the weight.csv files and .csv files should not be placed in other subdirectories.</li> <li>2.The videos for the corresponding weight sensor data can be found in the "Videos folder" in the repository and are named similar to their corresponding ".csv" files.</li> <li>3.Each video files consists of video data from 13 different camera angles.</li> </ol> Details of the weight sensor files: These weight.csv (Baseline cases and team particular cases ) files are from the AIFI CPS IoT 2020 week.There are over 50 cases in total and each file has 5 columns (Fig 5) (timestamp, reading (in grams), gondola, shelf, plate number). Each of these files have data of around 2-5 minutes or 120 seconds in the form of timestamp. In order to unpack date and time from timestamp use datetime module from python. Details of the product.csv files: There are product.csv files for each test cases and these files provide the detailed information about the product name, product location (gondola number, shelf number and plate number) in the store, product weight(in grams), also link to the image of the product. Instruction to run the script: To start analysing the weigh.csv files using the python script and plot the timeseries plot for corresponding files. <ol> <li>Download the dataset.</li> <li>Make sure to place the python/ jupyter notebook file is in same directory as the .csv files.</li> <li>Install the requirements <code>$ pip3 install -r requirements.txt</code></li> <li>Run the python script Plot.py <code>$ python3 Plot.py</code></li> </ol> After the script has run successfully you will find the corresponding folders of weight.csv files which contain the figures (weight vs timestamp) in the format Instruction to run the Jupyter Notebook: Run the Plot.ipynb file using Jupyter Notebook by placing .csv files in the same directory as the Plot.ipynb script. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------                                                                          gondola_number,shelf_number.png                                                                             Ex: 1,1.png (Fig 4) (Timeseries Graph)