Find research datasets worth reusing

openCC (other)Jul 2019View details →

LAGOS-NE-LIMNO v1.087.3: 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-LIMNO v1.087.3, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. With this release, only this data package is being updated and users are expected to use prior releases of the other types of data. Please see the attached additional documentation for a full description of the changes that have been made for this new release.The data packages that make up LAGOS-NE include the following information on lakes and reservoirs in 17 lake-rich states in the Northeastern and upper Midwestern U.S. (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes greater than one hectare. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes and for all spatial resolutions, also called ‘zones’ (i.e., ecoregions, states, counties). These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. (3) LAGOS-NE-LIMNO v1.087.3: 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. This module includes variables that are most commonly measured by state agencies and researchers for studying eutrophication. For each water quality data value, we also include metadata related to the sampling program, methods, qualifiers with data flags from the original program (qual, not standardized for LAGOS-NE), censor codes from our quality control procedures (censorcode, standardized for LAGOS-NE), and the date of each sample. (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-N

openCC (other)Oct 2025View details →

SBC LTER: Reef: Kelp Forest Community Dynamics: Transect geospatial metadata

The data table provides summaries of depth information (mean, standard deviation and coefficient of variation) for all of the transects surveyed as part of the SBC LTER Kelp Forest Monitoring program. All data are expressed in meters, reference to mean lower low water (MLLW). Each value is the result of 160 observations, four at each meter (n=160). The sampling locations in this dataset are 40 meter transects at nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. The PDF document provides descriptive information of the transect such as headings and relative locations of the points along the transect. Data were recorded in 2010 and 2011, except that IVEE transect 3, 4, 5, 6, 7, and 8 were recorded in 2024.

Gaviota Fire Perimeter (Santa Barbara County, CA), June 9, 2004 - From Geospatial Multi-Agency Coordination Group (GeoMAC)

The Gaviota Fire burned from 2004-06-05 to 2004-06-12, 15 miles west of Santa Barbara, Santa Barbara County. Approximately 7440 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2004-06-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.

Tea Fire Perimeter (Santa Barbara County, CA), November 15, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)

The Tea Fire burned from 2008-11-13 to 2008-11-17, Montecito, Cold Springs Creek and Hot Springs Road, Santa Barbara County. Approximately 1940 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-11-15, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.

Jesusita Fire Perimeter (Santa Barbara County, CA), May 10, 2009 - From Geospatial Multi-Agency Coordination Group (GeoMAC)

The Jesusita Fire burned from 2008-05-05 to 2008-05-18, Northwest of Mission Canyon and Santa Barbara City, Santa Barbara County. Approximately 8733 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-05-10, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.

opencc-by-4.0Nov 2021View details →

Modern China Geospatial Database - Main Dataset

MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21) You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp Altogether there are 464,970 entries. The data include seven variables: - Name:  Place names and their variants in Chinese, pinyin, and any recorded transliteration - Prov_Zh: Chinese province names in Chinese characters (新疆, 江蘇, 河北, etc.) - Prov_Py: Chinese province names in pinyin - LAT: Latitude coordinates - LONG: Longitude coordinates - LocID: Location identifiers - NameID: Location name identifiers The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD. Locations IDs that start with "D" are data points extracted from China Historical GIS (Harvard University); those that start with "E" are locations extracted from the data points in Geonames or data points we have added from various map sources. One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates. From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD.    UPDATES MCGD_Data2025_08_06 introduces a significant update with the addition of the ‘Code’ column. This column categorizes place names as follows: <ul> <li> A: Canonical Chinese name </li> <li> C: Alternative Chinese name </li> <li> P: Romanized name in pinyin </li> <li> W: Romanized name in another transliteration system </li> </ul> When the codes P or W are doubled (PP, WW), this indicates that the place name does not match any existing Chinese name in the dataset. These unmatched names will be reviewed and linked progressively, rather than through a systematic batch process, due to their high volume.The coding system is designed to facilitate name-matching operations between MCGD and place names extracted from historical sources using programming tools. It also enables filtering for more precise and efficient matching. The dataset contains a total of 472,749 entries. MCGD_Data2025_02_28 includes a major change with the duplication of all the locations listed under Beijing, Shanghai, Tianjin, and Chongqing (北京, 上海, 天津, 重慶) and their listing under the name of the provinces to which they belonge origially before the creation of the four special municipalities after 1949. This is meant to facilitate the matching of data from historical sources. Each location has a unique NameID. Altogether there are 472,818 entries MCGD_Data2025_02_27 inclues an update on locations extracted from  Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The file now includes 465,128 entries. MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.

opencc-by-4.0Nov 2021View details →

Modern China Geospatial Database - Republican China Dataset

MCGD_Rep is a sample of spatial data for China in the first half of twentieth century (1900-1949). The data was extracted from the MCGD Main Dataset. It is based mostly on the list of xian (county) seats in 1931 [Source: Zang, Lihe  臧励龢, ed. Zhongguo gujin diming da cidian 中国古今地名大辞典. Shanghai 上海: Commercial Press, 1931], with the addition of some external data [Source: Crow Newspaper Directories]. By and large, it presents a list of the major locations in China between 1900 and 1949. It contains 1,977 entries with the following variables: name in Chinese, name in pinyin; name of the province in Chinese and in pinyin; latitude and longitude, and Name ID and Location ID.

opencc-by-4.0Apr 2018View details →

Geospatial Modelling of Australia's National Electricity Market - Dataset

This dataset contains information relating to the topology of Australia's largest electricity transmission network, along with details pertaining to the technical and economic characteristics of generators operating within this grid. Information has been compiled from publicly available datasets released by the Australian Energy Market Operator (AEMO) [1, 2] and Geoscience Australia (GA) [3, 4, 5]. Potential applications include the development of economic dispatch, power-flow, and unit commitment models. The network is comprised of 912 nodes, 1406 AC edges, and three HVDC links. Information regarding forward and reverse power-flow limits for two AC interconnectors is also provided. Latitude and longitude coordinates are given for each node, with the network based off of geospatial datasets obtained from GA [3, 4, 5]. Signals for electricity demand at each node were derived using regional load profiles in combination with population data obtained from the Australian Bureau of Statistics (ABS) [6]. Allocation methods outlined in [7, 8] were used to disaggregate regional load profiles according to the geospatial distribution of Australia's population. Construction of the generator dataset involved compiling information obtained from AEMO's Market Management System Data Model (MMSDM) [1] and National Transmission Network Development Plan (NTNDP) [2] datasets. Historic generator dispatch signals were also obtained from AEMO [1], allowing the output of market models to be compared with realised outcomes. For further information regarding the contents of each csv file please refer to <code>dataset_summary.pdf</code>. Jupyter Notebooks at [9] contain the Python code necessary to reproduce these datasets. Version history: v1.3 - Documentation update: <ul> <li>The document summarising datasets, <code>dataset_summary.pdf</code>, has been updated.</li> </ul> v1.2 - Startup cost correction: <ul> <li>Startup cost column labels in <code>generators.csv</code> were mistakenly switched (specifically, SU_COST_WARM and SU_COST_HOT). This has now been corrected.</li> </ul> v1.1 - Transmission line parameters and demand allocation update <ul> <li>Transmission lines: Line resistance and shunt susceptance values have been updated. Transmission line lengths and voltages have also been added to <code>network_edges.csv</code>. AC interconnector information is split over two files to better capture aggregate flow limits defined over the New South Wales - Victoria interconnector. Connection points for these interconnectors are described in <code>network_ac_interconnector_links.csv</code>, while <code>network_ac_interconnector_flow_limits.csv</code> contains aggregate forward and reverse power flow limits.</li> <li>Demand allocation: The algorithm used to approximate demand at different nodes has been updated. The new method constructs a Voronoi tessellation based on network nodes. These cells are then overlapped with geospatial ABS population data, which are used to estimate the number of people served by each node.</li> </ul> v1.0 - First release

opencc-by-4.0Feb 2024View details →

A high-resolution 4D geospatial laser scan dataset of the beach at Mariakerke Bad, Belgium

This dataset contains a high resolution (in both time and space) laser scan data set of a 1-year measurement campaign in 2017 and 2018 in the seaside resort of Mariakerke Bad in Belgium. The measurements consist of 8417 hourly laserscans of a 400 meter stretch of beach. The measurement campained was performed to study variations in shoreward sand transport at urbanized beaches.  Laserscan data is stored in local coordinates. Time dependent corrections per laserscan epoch are provided next to a global transformation matrix to transform the local coordinates to the Belgium Lambert 2008 coordinate system. This data is provided as is and is licensed under the Creative Commons Attribution 4.0 International (CC-BY-4.0). See the provided PDF on more information about the CC-BY-4.0. Version 1 contained an error in the global transformation matrix. Version 2 corrects this.

opencc-by-4.0Apr 2021View details →

Geospatial Input Datasets for Urban Teleconnections Analysis

This dataset includes all of the necessary input layers for the `gamut` software package. Layers were downloaded from the sources in the references and were either directly used in the `gamut` software or processed prior to being used in the software. Processing and data information for each dataset is included in the metadata.

opencc-by-4.0Jul 2023View details →

Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level

Pan-EU Land Mask Summary Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>. There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>. The masks are: <ol> <li> Landmask </li> <li> ISO-code country mask </li> <li> NUTS3 mask </li> </ol> Name convention To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include: <ol> <li> Generic variable name: country.code </li> <li> Variable procedure combination i.e. method standard (standard abbreviation): iso.3166 </li> <li> Position in the probability distribution / variable type: c </li> <li> Spatial support (usually horizontal block) in m or km: 30m </li> <li> Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s </li> <li> Time reference begin time (YYYYMMDD): 20210101 </li> <li> Time reference end time: 20211231 </li> <li> Bounding box (2 letters max): eu  </li> <li> EPSG code: epsg.3035 </li> <li> Version code i.e. creation date: v20230722 </li> </ol> An example of a file-name based on the description above: country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722 Landmask The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies. Two reference datasets are used,  <ol> <li> <a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution. </li> <li> <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask. </li> </ol> When generating the land mask, the two reference datasets in a way that: <ul> <li> If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask.  </li> <li> Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water. </li> </ul> The landmask consists of 4 values: <ul> <li> 10: not in the pan-EU area, i.e. out of mapping scope </li> <li> 1: land </li> <li> 2: inland water </li> <li> 3: ocean </li> </ul> This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible. ISO-3166 country code mask This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country. The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL. NUTS-3 mask The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions. The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL. It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.

openCC0Apr 2017View details →

LAGOS-NE-LOCUS v1.01: 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-LOCUS v1.01, 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 v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NEGEO v1.05: 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 v1.087.1: 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-NE-GEO 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-LOCUS v1.01 module includes information on the physical location and features of all lakes > 4 ha. The information provided for this population of lakes includes: lake unique identifiers, lake area, perimeter, latitude and longitude, and the zone IDs that the lake is located within (e.g., state, county, the hydrologic unit at each level (4, 8, and 12). Citation for

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

openCC (other)Apr 2023View details →

Geospatial data for Luquillo Mountains, Puerto Rico: Mean annual precipitation, elevation, watershed outlines, and rain gage locations

The data archive is here: https://doi.org/10.5066/F74F1PM2 please use this DOI when citing this data set. These geospatial data sets were developed as part of a new analysis of all known current and historical rain gages in the Luquillo Mountains, Puerto Rico published in the journal article Murphy, S.F., Stallard, R.F., Scholl, M.A., Gonzalez, G., and Torres-Sanchez, A.J., 2017, Reassessing rainfall in the Luquillo Mountains, Puerto Rico: Local and global ecohydrological implications: PLOS One 12(7): e0180987, p. 1-26, https://doi.org/10.1371/journal.pone.0180987. That article provides a revised map of mean annual precipitation developed using elevation regression functions and residual interpolation, and that map is presented here in a raster file. Most previous forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation in the Luquillo Mountains; therefore, precipitation in leeward Luquillo watersheds has been overestimated by up to 40%.Because the Luquillo Mountains often serve as a wet tropical archetype in global assessments of basic ecohydrological processes, these revised estimates are relevant to regional and global assessments of runoff efficiency, hydrologic effects of reforestation, geomorphic processes, and climate change. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.\</para\>

SBC LTER: Reef: Geospatial structure of microsatellite markers for the giant kelp, Macrocystis pyrifera, Santa Barbara CA, 2009

Data package includes data and R code for the examination of geospatial genetic structure (SGS) and simulation of inbreeding in giant kelp (Macrocystis pyrifera) from the Santa Barbara Channel, California. Data are reported for microsatellite markers from individual giant kelp plants from Carpinteria Reef, Mohawk Reef and Goleta Bay, collected during September 2009. Relative X and Y coordinates (meters) were recorded for each specimen sampled. R code consists of two scripts, a) to calculate mean number of alleles per locus, and observed and expected heterozygosity, and b) to simulate self-fertilization and sibling/cousin relationships. These data were presented in: Johansson, Mattias L, Raimondi, Peter T, Reed, Daniel C, Coelho, Nelson C, Serrão, Ester A, Alberto, Filipe A. In press. Looking into the black box: simulating the role of self-fertilization and mortality in the genetic structure of Macrocystis pyrifera. Molecular Ecology, 22:4842–4854. These data are also available from: Johansson ML, Raimondi PT, Reed DC, Coelho NC, Serrão EA, Alberto FA (2013) Data from: Looking into the black box: simulating the role of self-fertilization and mortality in the genetic structure of Macrocystis pyrifera. Dryad Digital Repository. doi:10.5061/dryad.s1b07.