Find research datasets worth reusing

opencc-by-4.0Oct 2019View details →

A comprehensive data-based assessment of forest ecosystem carbon stocks in the U.S. 1907-2012

This excel file contains data on forest ecosystem Carbon stocks in the United States from 1907-2012 used to create figures 2 (a) (b), 3, 4, 5 (a) (b) presented in the article "A comprehensive data-based assessment of forest ecosystem carbon stocks in the U.S. 1907-2012".

opencc-by-4.0Dec 2013View details →

Fig. 2. Haplotype network calculated from the E in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa

Fig. 2. Haplotype network calculated from the E. andrei COI haplotypes found in the South African earthworm groups investigated. The size of the circles is proportional to the number of earthworms sharing the same haplotype. The numbers on the branches indicate the positions of mutations on the COI sequences, mv1 represents a median vector (intermediate haplotypes, not found in this study).

opencc-by-4.0Dec 2013View details →

Fig. 1 in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa

Fig. 1. Neighbour-joining tree based on the K2P method. Bootstrap support obtained for specific nodes are reported. Genbank accession numbers or BOLD process IDs are provided in brackets for the sequences downloaded from either Genbank or BOLD. Allolobophoridella eiseni and Microscolex phosphoreus were included as outgroups. Asterisk indicates dubious E. andrei sequences from BOLD.

Fig. 8 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 8: Time trajectories from 2002 to 2015 and forecasting for the next 20 years (2016-2036) of the relative fishing mortality rate (F/Fmsy) and relative population biomass (B/Bmsy) estimated using a non-equilibrium surplus production model, setting maximum sustainable yield (MSY) at a level corresponding to the value of the latest year. The dotted lines represent the upper and lower 80% confidence limits.

Fig. 7 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 7: Trajectories from 2002 to 2015 of relative fishing mortality and biomass for Callista chione in the northern Alboran Sea.

Fig. 3 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 3: Distribution of artisanal fisheries by fishing gear and landing port from March 2013 to March 2014. Values are given as percentage contribution of each fishery in tonnes.

Fig. 4 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 4: Number of vessels by landing port targeting bivalves with mechanised dredges, in the northern Alboran Sea.

Fig. 1 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 1: Geographical subareas of the General Fisheries Commission for the Mediterranean (GFCM) and map of the study area in the northern Alboran Sea (W Mediterranean Sea), showing the locations of the fishing ports.

Fig. 5 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 5: Evolution of catches and CPUE for Callista chione in the northern Alboran Sea between 2001 and 2015.

Fig. 2 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 2: Number and average technical characteristics (gross tonnes, length, and engine power) of artisanal vessels per landing port along the Alboran Sea.

opencc-by-4.0Jun 2024View details →

Mediterranean risk assessment data based on the concurrency between climate change, fisheries, stocks, and biodiversity

Data associated to the paper "Detecting Ecosystem Risk Hotspots: A Mediterranean Case Study" by G. Coro, L. Pavirani, A. Ellenbroek.

opencc-by-4.0Jan 2021View details →

Extended RAM Legacy Stock Assessment Database version 4.493

Extended RAM Legacy Stock Assessment Database version 4.493 Please be aware that this ‘mdl’ ('model fits included') version 4.493 of the RAM Legacy Stock Assessment Database (RAMLDB) additionally includes data not contained in the assessments themselves, but generated from assessment data. Generated data are used to fill in gaps where possible, for example, if stock assessments do not include reference point estimates. Supplemented data include calculations from assessment values, conversions of biomass variables, and reference points estimated post-hoc with surplus production models. The ‘Documents’ folder includes documentation files on the methods used for these calculations, conversions and model fitting. When assessment data are available, they are used preferentially in all cases. Although we identify data that were supplemented from data that were derived from stock assessments, some collector variables in RAMLDB include both of these data sources (with assessment values preferred). We recommend that the average user does not use this 'mdl' version of RAMLDB because of the additional assumptions and calculations involved. Instead, to avoid confusion between assessment-derived values and supplemented values, we recommend using the ‘asmt’ ('assessment data only') version of RAMLDB version 4.493. This 'asmt' version (doi 10.5281/zenodo.4458244) is also available in our Zenodo repository: <a href="https://zenodo.org/communities/rlsadb/">https://zenodo.org/communities/rlsadb/</a>

opencc-by-4.0Jul 2017View details →

RAM Legacy Stock Assessment Database Geospatial Regions

This data archive describes region definitions for the RAM Legacy Stock Assessment Database.  Within the RAM Legacy database, stock assessments are associated with named areas. We approximate coordinates and bounding boxes for each of these areas, using country EEZs and fishing area shapefiles when appropriate.  In addition, we develop a simple language to encode the GIS shapes of the areas, along with an interpreter to translate these codes into polygons. The syntax supports using political entities, shapefile regions, circles and rectangles, clipped versions of these, and combinations of these. The archive contains the following contents:  - syntax.pdf: This document describes the geocoding syntax, and lists all of the geocoding descriptions for the assessment regions.  - results: This folder contains a shapefile of assessment regions (ram.shp) and a summary file of each region's centroid and size.  - sources: This folder contains shapefiles for FAO regions and New Zealand fishing regions, used by the syntax system, and latlon.csv which contains the region descriptions for each assessment region.  - code: load_areas.R contains functions that interpret the geocoding syntax and genshape.R generates the ram.shp shapefile.

opencc-by-4.0Nov 2021View details →

Extended RAM Legacy Stock Assessment Database version 4.495

Extended RAM Legacy Stock Assessment Database version 4.4945 Please be aware that this ‘mdl’ ('model fits included') version 4.495 of the RAM Legacy Stock Assessment Database (RAMLDB) additionally includes data not contained in the assessments themselves, but generated from assessment data. Generated data are used to fill in gaps where possible, for example, if stock assessments do not include reference point estimates. Supplemented data include calculations from assessment values, conversions of biomass variables, and reference points estimated post-hoc with surplus production models. The ‘Documents’ folder includes documentation files on the methods used for these calculations, conversions and model fitting. When assessment data are available, they are used preferentially in all cases. Although we identify data that were supplemented from data that were derived from stock assessments, some collector variables in RAMLDB include both of these data sources (with assessment values preferred). We recommend that the average user does not use this 'mdl' version of RAMLDB because of the additional assumptions and calculations involved. Instead, to avoid confusion between assessment-derived values and supplemented values, we recommend using the ‘asmt’ ('assessment data only') version of RAMLDB version 4.495. This 'asmt' version (doi 10.5281/zenodo.4824192) is also available in our Zenodo repository: <a href="https://zenodo.org/communities/rlsadb/">https://zenodo.org/communities/rlsadb/</a>

opencc-by-4.0Dec 2020View details →

Data from: Socio-ecological drivers of long-term ecosystem carbon stock trend: An assessment with the LUCCA model of the French case

Open the record for dataset details and reuse information.

dryad36/100

Indian Ocean Tuna Commission yellowfin tuna stock synthesis assessment grid

The Tuna Regional Fisheries Management Organisations commonly develop uncertainty grids to condition models in integrated stock assessments to account for uncertainties in parameters that cannot be estimated from the data. For example, by constructing multidimensional grids that consist of different plausible combinations of assumptions, fixed parameter values, and data sets. It is not always clear, however, whether this is intended as an uncertainty analysis or a sensitivity analysis. The choice of assessment model scenarios and ways of estimating uncertainty has an impact on the risk of exceeding the limit and missing target reference points. Therefore, to better understand the impact of uncertainty on stock assessment advice and the risk of failing to meet conservation and sustainability objectives, we used the uncertainty grid developed by the Indian Ocean Tuna Commission (IOTC) for albacore tuna (Thunnus alalunga), a full factorial design with 1,440 model configurations (IOTC, 2019). This is sufficient to provide contrast, but not too big to be unmanageable. IOTC. 2019 (23-27 July 2019). Report of the 7th Session of the IOTC Working Party on Temperate Tunas: Assessment Meeting. Tech. rept. IOTC-2019-WPTmT07(AS)-R. Indian Ocean Tuna Commission, Shizuoka, Japan

opencc-zeroJun 2024View details →

Fig. 6 in Exploitation status and stock assessment of the smooth clam Callista chione (Linnaeus, 1758) in the northern Alboran Sea (GSA01-W Mediterranean Sea)

Fig. 6: Trajectories from 2002 to 2015 of observed CPUE andCPUE estimated using the ASPIC model.

openodc-pddlDec 2019View details →

Data on stock assessment and catches of blue whiting in Northeast Atlantic and adjacent waters 1981-2019

Data on stock assessment and catches of blue whiting in Northeast Atlantic and adjacent waters 1981-2019 used for development of Climate Adaptation Plans (CAPs) in the Northeast Atlantic pelagic case study in the ClimeFish project

opencc-by-4.0Aug 2024View details →

Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa

Dataset Name: Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa. Description: This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results. Files: The following files are included in the dataset: <ul> <li>    [CountryName]_LiteratureSources.xlsx: Excel sheet containing literature sources and references,</li> <li>    [CountryName]_ArchetypeParameters.xlsx: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li>    VacantHouses.xlsx: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li>    Resource Use Results: BuildME Simulation Results</li> </ul> Usage: The dataset is intended for researching and analyzing the Western Asian and North African countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings. License: The dataset is licensed under Creative Commons Attribution 4.0 International. Citation: If you use this dataset in your research, please cite it as follows and contact the corresponding author: Akin, Sahin, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa”  https://doi.org/10.5281/zenodo.13380340. Contact: The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact sahin.akin@ntnu.no, the corresponding author.