Find research datasets worth reusing

openCC (other)Jun 2023View details →

Frugivoria: A trait database for birds and mammals exhibiting frugivory across contiguous Neotropical moist forests

Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, morphological, and geographic traits, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria contains mammal and bird species found in contiguous moist montane forests and adjacent moist lowland forests of Central and South America– the latter specifically focusing on the Andean states. In total, Frugivoria includes 45,216 unique trait values, including new values and harmonized values from existing databases. Frugivoria adds 23,707 new trait values (8,709 for mammals and 14,999 for birds) for a total of 1,733 bird and mammal species. These traits include diet breadth, habitat breadth, habitat specialization, body size, sexual dimorphism, and range-based geographic traits including range size, average annual mean temperature and precipitation, and metrics of human impact calculated over the range. Frugivoria fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity, and extends certain traits, once only a

openCC (other)Jan 2025View details →

A database of published mangrove articles for coastal Louisiana, USA

Mangroves are being increasingly recognized as natural climate solutions for the range of ecosystem services they provide. In North America, one of the northern range limits of mangroves is found in coastal Louisiana, USA, where in recent decades, mangroves have been expanding into wetlands formerly dominated by salt marsh primarily due to decreases in the frequency and severity of winter freeze events. While reviews focused on mangrove ecology that include coastal Louisiana within a broader geographic scope have been conducted, no systematic review has focused on what is known about mangrove ecology across coastal Louisiana, a region that contains the expansive Mississippi River Delta. To fill this knowledge gap, we conducted a systematic review to highlight the breadth of mangrove research topics that have been studied in coastal Louisiana and identify emerging and future research opportunities. We identified four main research topics: (1) mangrove expansion, (2) freeze tolerance, (3) coastal restoration, and (4) disturbance. We also identified geographic biases in where mangrove research has been conducted, with a focus around the heavily industrialized Port Fourchon/Grand Isle area.

openCC (other)Nov 2021View details →

Global N2O Database version 1.0

Agriculture is the primary source of the powerful greenhouse gas (GHG) nitrous oxide (N2O) and an important source of GHG emissions. Due to sampling limitations, N2O measurements have traditionally been sparse; with research studies that often have less than 50 sampled days within a year. Nitrous oxide emissions are highly variable and short-lived peak emission periods may contribute more than 50% to annual emissions. Gap filling around these peaks, if measured at all, can result in poor estimations under the standard practice using linear interpolation. Improved gap filling methods that reflect covariate data will likely reduce uncertainty and improve annual N2O estimates. The Global N2O Database was created to serve as a repository for these datasets as well as become a resource for publicly available data and analytical advances. These datasets have been joined in data sheets that use the same formatting, allowing for easy access and comparison of data sets. We hope that this data availability will lead to improvements in N2O understanding and mitigation.

openCC0May 2017View details →

LAGOS-NE-GEO v1.05: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013

This data package, LAGOS-NE-GEO v1.05, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NEGEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE-GEO v1.05 module includes information on the ecological context of the census lakes, all lakes > 4 ha in the study extent, their watersheds, and their regions. The information provided in the data tables for this module is organized into three main themes: CHAG - climate, hydrology, atmospheric deposition of nitrogen and sulfur, and surficial geology; LULC - land use/cover, impervious co

openCC0Jun 2022View details →

Harmonized Soil Database of Ecuador 2021

In Ecuador there have been two main projects that have collected national soil information. These projects are: a) “Generación de Geoinformación para la Gestión de territorio y valoración de tierras rurales de la Cuenca del Río Guayas, escala 1:25.000” (2007-2015) developed by the Instituto Espacial Ecuatoriano (IEE), and b) “Generación De Geoinformación Para La Gestión Del Territorio A Nivel Nacional" (2009-2012), developed by Sistema Nacional de Información de Tierras Rurales e Infraestructura Tecnológica (SIGTIERRAS). These projects followed a similar methodology to collect and analyze soil information. However, the resulting databases have different data structures, and they show differences in the way these projects store and present soil information. Only a portion of the original databases was digitized. Most of the available data was only available in PDF files. These PDF files need to be digitized into an easy-to-manage format (e.g., *csv). The difficulty is that each PDF represents one soil profile containing morphological and analytical soil information. Thus, given the volume of soil information available in hundreds of PDF files, manual extraction (e.g., capturing soil data one by one) was not feasible. Therefore, automatic extraction of soil information from each PDF file was developed using open-source programming for data management and statistical computing (in Python and R). This process was developed to optimize data extraction from PDF files. The soil information from both projects in PDF files has been digitized and unified into one harmonized database. We present a new database for Ecuador containing soil information from 13,542 soil profiles, 5 368 are from the IEE project and 8 174 profiles from the SIGTIERRAS project. The new database includes 5368 are from the IEE project and 8174 profiles from the SIGTIERRAS project. The new database includes data from 51,692 soil horizons and information of about 20 morphological and 46 analytical variabl

openCustomJan 2020View details →

A Comprehensive Radiocarbon Date Database from Archaeological Contexts on the Coastal Plain of Georgia

This database consists of radiocarbon dates from archaeological contexts on the coastal plain of Georgia (samples from strictly geological contexts were not included). A comprehensive search was performed to find the original sources of dates reported for all archaeological sites in this area. As such, dates reported from any time (i.e., 1960s to the present) were incorporated, which includes dates with problems. Data were compiled by John Turck and numerous undergraduate work study and volunteer students over a large period of time (from January 2012 to July 2012, and from January 2013 to April 2013). John Turck added to and refined the dataset between May 2013 and February 2014. In general, the database was structured so the information could be easily input into CALIB's online calibration program. It includes information such as: sample IDs, raw age and standard deviation, delta 13 correction factor, adjusted age and standard deviation, site number and name, material, association, and references. Calibrated dates were not entered into this databse. These data can be used to aid in archaeological studies, refining our understanding of the timing of human occupations throughout the coastal plain, and especially in the coastal zone. These data can also be used to aid geological, and geomorphological studies. The nature of the data is such that it will need to be continually added to as new samples are processesd, and further refined as more information about dates entered previosuly are obtained. Note: The original radiocarbon date database contains sensitive information (i.e., the specific location of archaeological sites) that is for professional archaeologists only. If a professional archaeologist needs site location information, they can contact the Georgia Archaeological Site File.

In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)

Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications. In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against in silico libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later). For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.

Compound database and subsets generated by the fragment network for stage 3 of the PHIP2 SAMPL7 Challenge

The fragment network provides a convenient way to filter-out compounds that are dissimilar to the input hit(s). Overall, this search algorithm requires a compound input and 3 parameters: 1- the number of graph traversals (hops), 2- number of changes in heavy atom count (hac), 3- number of changes in ring atoms counts (rac).  Please, read the reference (Hall, Murray and Verdonk, 2017) for the specifics of the methods.

Enzymes from the BRENDA and CAZy databases annotated with organism growth temperatures and predicted Topt

This repo is an updated version of repo Gang Li, & Martin KM Engqvist. (2019). Enzymes from the BRENDA database annotated with organism growth temperatures and predicted Topt (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2539114.  Experimental as well as predicted organism growth temperatures were used to annotate enzymes from the BRENDA database (doi: 10.1093/nar/gky1048, https://www.brenda-enzymes.org) version 2018.2 (July 2018) and CAZy database (http://www.cazy.org/).   An updated machine learning model was applied to predict the optimal functional temperature of enzymes from BRENDA and CAZy.  There are four files in this repo: 1. 'annotated_brenda.tsv' is a tab-seperated file that contains the annotated enzymes from BRENDA. There are 9 columns in the file: index column; "ec", EC number; "uniprot_id", protein id in Uniprot database; "domain", the domain of life (superkingdom), either Archaea, Bacteria, or Eukarya; "organism", species name; "ogt", optimal growth temperature of the organism; "ogt_note", whether the experimental or predicted ogt is used; "topt", the optimal functional temperature of the enzyme; "topt_note", whether the experimental or predicted topt is used. 2. 'annotated_cazy.tsv' is a tab-seperated file that contains the annotated enzymes from CAZy. There are 12 columns in the file: index column; "family", CAZy family id; "genbank", genbank id; "Protein Name", the protein name from CAZy database; "ec", EC number; "organism", strain name; "uniprot_id", protein id in Uniprot database; "PDB/3D", structure id in PDB database; "ogt", optimal growth temperature of the organism; "ogt_note", whether the experimental or predicted ogt is used; "topt", the optimal functional temperature of the enzyme; "topt_note", whether the experimental or predicted topt is used. 3. 'brenda.sql', which is a SQLite3 database version of 'annotated_brenda.tsv', with an additional column of enzyme sequences. 4. 'cazy.sql', which is a SQLite3 database version of 'annotated_cazy.tsv'', with an additional column of enzyme sequences. The SQLite3 databases are for the Tome tool (<a href="https://github.com/EngqvistLab/Tome">https://github.com/EngqvistLab/Tome</a>), version 2.0.

ACF database on the vitamin A and iron outcomes from the MANGO trial

This dataset contains the variables used in the analysis of the vitamin A and iron outcomes of the MANGO trial carried out in Burkina Faso between 2016 and 2018.

PROSEU Collective Renewable Energy Prosumers Stakeholders Database (Template)

As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.

Earth - Venus Low-Thrust Optimal Transfers / Database A

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of 0.2 and contains 429,316 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

Earth - Venus Low-Thrust Optimal Transfers / Database F

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 557,395 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

Earth - Venus Low-Thrust Optimal Transfers / Database E

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of 20.0 and contains 409,076 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

Earth - Venus Low-Thrust Optimal Transfers / Database D

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of 5.0 and contains 265,603 trajectories with 128 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

Earth - Venus Low-Thrust Optimal Transfers / Database C

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of 0.4 and contains 764,479 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

Earth - Venus Low-Thrust Optimal Transfers / Database B

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of 0.4 and contains 382,193 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

Earth - Venus Low-Thrust Optimal Transfers / Database G

A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit.  This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 999,985 trajectories with 100 samples along each trajectory. The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format: <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function. We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.

opencc-zeroFeb 2020View details →

EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database

EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.