Find research datasets worth reusing

AfriSAR: Canopy Cover and Vertical Profile Metrics Derived from LVIS, Gabon, 2016

This dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle.

GEDI L3 Gridded Land Surface Metrics, Version 1

This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation and counts of laser footprints per 1 km x 1 km grid cells globally within -52 and 52 degrees latitude. L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models. This first release of L3 products are simple averages and standard deviations of the footprint profile metrics within each 1 sq. km cell. Future versions will optimally interpolate data to produce the best estimate of the mean and its error for each grid cell.

Gridded GEDI Vegetation Structure Metrics and Biomass Density at Multiple Resolutions

This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data. The GEDI sensor, mounted on the International Space Station (ISS), uses eight laser beams spaced by 60 m along-track and 600 m across-track on the Earth surface to measure ground elevation and vegetation structure between approximately 52 degrees North and South latitude. Between April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7 billion quality waveforms suitable for measuring ground elevation and vegetation structure, respectively. This dataset provides GEDI shot metrics aggregated into raster grids at three spatial resolutions: 1 km, 6 km, and 12 km. In addition to many of the standard L2 and L4A shot metrics, several additional metrics have been derived which may be particularly useful for applications in carbon and water cycling processes in earth system models, as well as forest management, biodiversity modeling, and habitat assessment. Variables include canopy height, canopy cover, plant area index, foliage height diversity, and plant area volume density at 5 m strata. Eight statistics are included for each GEDI shot metric: mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, 95th percentile, Shannon's diversity index, and shot count. Quality shot filtering methodology that aligns with the GEDI L4B Gridded Aboveground Biomass Density, Version 2.1 was used. In comparison to the current GEDI L3 dataset, this dataset provides additional gridded metrics at multiple spatial resolutions and over several temporal periods (annual and the full mission duration). Files are provided in cloud optimized GeoTIFF format.

GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V002

The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_B data product contains 96 layers for each of the eight-beam ground transects (or laser footprints located on the land surface). Datasets provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the layers can be found in the GEDI Level 2B Data Dictionary.Known Issues* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).* Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this [document](https://lpdaac.usgs.gov/documents/2236/GEDI_CORRECTED_RGT_FILENAMES.pptx) for the correct RGT numbers.* Known Issues: Section 8 of the User Guide provides additional information on known issues.Improvements/Changes from Previous Versions* Metadata has been updated to include spatial coordinates.* Granule size has been reduced from one full ISS orbit (~1.19 GB) to four segments per orbit (~0.30 GB).* Filename has been updated to include segment number and version number.* Improved geolocation for an orbital segment.* Added elevation from the SRTM digital elevation model for comparison.* Modified the method to predict an optimum algorithm setting group per laser shot.* Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.* Added selected_mode_flag dataset to root beam group using selected algorithm.* Removed shots when the laser is not firing.* Modified file name to include segment number and dataset version.

AfriSAR: Gridded Forest Biomass and Canopy Metrics Derived from LVIS, Gabon, 2016

This dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016. Derived canopy cover, canopy heights, bare ground elevation, plant area index (PAI), and foliage height diversity (FHD), and respective uncertainties are provided at a 25 m resolution for each of the five study sites. Aboveground biomass density (AGBD) and uncertainty were modeled at 50 m and 100 m resolutions for the Lope, Mondah, and Mabounie sites using field inventory data and waveform height and cover metrics. Lidar grid cell data collection statistics (i.e., number of shots and flight lines) and a data mask are also included. This research leverages high-quality forest inventory datasets collected during the AfriSAR campaign for one of the least studied and most unique forest ecosystems in the world.

Gridded GEDI-Fusion Forest Structure Metrics across Six Western US States, 2016-2020

This dataset provides eight GEDI forest structure metrics relevant to wildlife habitat modeling and biodiversity assessments at 30-m resolutions across Washington, Oregon, Idaho, Montana, Wyoming, and Colorado. The metrics characterize canopy height, strata densities, and canopy cover. The data were derived using random forest modeling and prediction frameworks. The models created were also hindcasted using 2019 and 2020 GEDI footprints back to 2016 on annual time steps leveraging continuous Landsat spectral and disturbance information, Sentinel-1 backscatter metrics and ratios, topographic information, and bioclimatic variables. Machine learning data fusion approaches were used to scale-up structure information provided by the novel space-borne Global Ecosystems Dynamics Investigation (GEDI) waveform lidar sensor to continuous extents using additional satellite-based continuous earth observation data. GEDI provides a consistent sample of forest structure information at 25-m diameter footprints at near-global extents, providing a valuable source of reference information to drive continuous mapping efforts.

openGEO-OpenFeb 2012View details →

geo24/100

Highly Parallel Genome-Wide Expression Analysis of Single Mammalian Cells (Performance Metrics)

GEO Series GSE34363. Homo sapiens. 33 samples. Type: Expression profiling by array.

openmit-licenseFeb 2020View details →

Contribution and Quality Metrics for Quantifying the Software Development Process Dataset

This dataset contains the Contributions and Quality data regarding the 3,000 most starred GitHub Java projects towards Quantifying the Software Development Process. You can use the dataset simply with the following steps:    1. Download the data.    2. Navigate to the download folder and use the mongorestore (<a href="https://docs.mongodb.com/manual/reference/program/mongorestore/">https://docs.mongodb.com/manual/reference/program/mongorestore/</a>) command. (Have in mind to use the --gzip flag)

opencc-by-4.0Apr 2020View details →

Smart Contracts Corpus Repository: Source Code and Metrics.

Many empirical software engineering studies show that there is a great need for repositories where code is acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search Ethereum blockchain data such as transactions, addresses, tokens, smart-contracts' source code, prices and other activities taking place on Ethereum blockchain. Despite the availability of this kind of services, retrieving specific information useful to empirical software engineering studies, such as the study of smart-contracts' software metrics, might be a tedious task that requires different sub-tasks such as searching specific transactions in a block, parsing files in HTML format and filtering the smart-contracts to remove duplicated code or unused smart-contracts. Smac-Corpus aims to create a smart-contracts' repository where smart contracts data (source code, ABI and byte code) are freely and immediately available and also classified based on software metrics identified in the scientific literature.

opencc-by-4.0Feb 2020View details →

Machine learning for buildings' characterization and power-law recovery of urban metrics

We focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings’ number of floors and construction periods’ dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying on a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow.

dryad24/100

Data from: Measuring and interpreting sexual selection metrics - evaluation and guidelines

(1) Routine assessments of overall sexual selection, including comparisons of its direction and intensity between sexes or species, rely on summary metrics that capture the essence of sexual selection. Nearly all currently employed metrics require population-wide estimates of individual mating success and reproductive success. The resulting sexual selection metrics, however, can heavily and systematically vary with the chosen approaches in terms of sampling, measurement, and analysis. (2) Our review illustrates this variation using the Bateman gradient, a particularly prominent sexual selection metric. It represents the selection gradient on mating success and – given the latter's pivotal role in defining sexual selection – reflects a trait-independent integrative proxy for the maximum strength of sexual selection. Drawing from a recent meta-analysis, we evaluate potential biases arising from study design, data collection, and parameter estimation, and provide suggestions to mitigate such biases in future studies. (3) With respect to study design, we argue that currently almost inexistent manipulative studies must complement the dominating correlative studies to inform us about causality in sexual selection. With respect to data collection, we outline how different measures of mating and reproductive success affect the components of sexual (and natural) selection that are reflected in standard summary metrics. With respect to parameter estimation, we show the potential impact of decisions about data inclusion and the chosen quantitative approach on inferences of sexual selection and its sex difference. (4) We expect this meta-analytical review to aid future studies in providing less biased and more informative estimates of sexual selection.

opencc-zeroDec 2015View details →

opencc-by-4.0Nov 2023View details →

Metrics of neural network models in 2022 crops in Extremadura, Spain

Characteristics of the 32 neural network models generated through the analysis of Sentinel-2 images in crops in Extremadura in 2022.

Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y5 & Y6

A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2000 (Y5) and 2001 (Y6).

Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y19 & Y20

Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y19 & Y20

A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2014 (Y19) and 2015 (Y20).

Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 1000m Y5 & Y6

A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 1000m particles releases for the years 2000 (Y5) and 2001 (Y6).

opencc-by-4.0Aug 2022View details →

Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations

The data provided here has been used to create the figures in the paper submitted to Biogeosciences titled Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations.

opencc-by-4.0Jul 2024View details →

A proposal of metrics to automatically populate a user model for intelligent user interfaces in MDD: Experimental material

Material of paper: A proposal of metrics to automatically populate a user model for intelligent user interfaces in MDD

opencc-by-4.0Apr 2024View details →

Dataset: Advanced Similarity Metrics for IP Flow Data Analytics

Analysis of encrypted traffic in computer networks is intricate due to reduced visibility in transmitted content. Machine-learning techniques applied to data representing characteristics of traffic flows provide powerful tools for network monitoring or intrusion detection.  Since real-world datasets are scarce, we present a novel traffic classification dataset with TLS traffic. The dataset contains three days (19-08-2022 -- 21-08-2022) of anonymized communication on CESNET3 ISP network, which is used by approximately half a million users daily. Ethic statement The privacy of the CESNET network users is a fundamental concern in our work, leading us to conduct our research with careful consideration. The indisputable advantages of real traffic generated by hundreds of thousands of people come with understandable privacy concerns. Thus, we used only automatic data processing with immediate data anonymization. With this, we declare that we did not analyze or manually process non-anonymized data or perform any procedures that could allow us to track users or reveal their identities.  Data description The dataset consists of network flows describing encrypted TLS communications. Flows are extended with packet sequences, histograms, and fields extracted from the TLS ClientHello message, which is transmitted in the first packet of the TLS connection handshake. The most important extracted handshake field is the SNI domain, which is used for ground-truth labeling.  Packet Sequences Sequences of packet sizes, directions, and inter-packet times are standard data input for traffic analysis. For packet sizes, we consider the payload size after transport headers (TCP headers for the TLS case). We omit packets with no TCP payload, for example ACKs, because zero-payload packets are related to the transport layer internals rather than services’ behavior. Packet directions are encoded as ±1, where +1 means a packet sent from client to server, and -1 is a packet from server to client. Packet timing depends on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate a response. Packet sequences have a maximum length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time-stamps, and TCP flags.  Flow statistics Each data record also includes standard flow statistics, representing aggregated information about the entire bidirectional connection. The fields are the number of transmitted bytes and packets in both directions, the duration of the flow, and packet histograms. The packet histograms include binned counts (not limited to the first 30 packets) of packet sizes and inter-packet times in both directions. There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes (More information in the <a href="https://github.com/CESNET/ipfixprobe/tree/master#phists">PHISTS plugin documentation).</a>  Dataset structure The dataset is organized per individual days and hours. The flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. The following list describes flow data fields in CSV files: <ul> <li>TCP_FLAGS: Logical OR of all TCP flags transmitted from client to server</li> <li>TCP_FLAGS_REV: Logical OR of all TCP flags transmitted from server to client</li> <li>TLS_SNI: Server Name Indication domain</li> <li>TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li> <li>TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li> <li>DURATION: Duration of the flow in seconds</li> <li>BYTES: Number of transmitted bytes from client to server</li> <li>BYTES_REV: Number of transmitted bytes from server to client</li> <li>PACKETS: Number of packets transmitted from client to server</li> <li>PACKETS_REV: Number of packets transmitted from server to client</li> <li>PPI_PKT_DIRECTIONS: Direction of PPI sequence </li> <li>PPI_PKT_FLAGS: TCP flags of PPI sequence</li> <li>PPI_PKT_TIMES: Timestamps of individual packets in PPI sequence</li> <li>PPI_PKT_LENGTHS: Lengths of individual packets in PPI sequence</li> <li>S_PHISTS_SIZES: Histogram of packet sizes from client to server</li> <li>D_PHISTS_SIZES: Histogram of packet sizes from server to client</li> <li>S_PHISTS_IPT: Histogram of inter-packet times from client to server</li> <li>D_PHISTS_IPT: Histogram of inter-packet times from server to client</li> </ul>   The dataset also contains a service map in the form of a CSV file. The service map can be used to extract high-level labels from SNI domain names.    The directory tree of the dataset is: <pre><code>. ├── 20220819 │ ├── flows.202208190000.csv │ ├── flows.202208190100.csv | ├── ... │ └── flows.202208192300.csv ├── 20220820 │ ├── flows.202208200000.csv │ ├── flows.202208200100.csv | ├── ... │ └── flows.202208202300.csv └── 20220821 ├── flows.202208210000.csv ├── flows.202208210100.csv ├── ... └── flows.202208212300.csv </code></pre>