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AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations

This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations

This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations

This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

opencc-by-4.0Oct 2013View details →

Supplementary material 2 from: Bayliss H, Stewart G, Wilcox A, Randall N (2013) A perceived gap between invasive species research and stakeholder priorities. NeoBiota 19: 67-82. https://doi.org/10.3897/neobiota.19.4897

Journal article classifications (doi: 10.3897/neobiota.19.4897.app2) File format: Comma Separated Value File (csv).:

opencc-by-4.0Oct 2013View details →

Supplementary material 1 from: Bayliss H, Stewart G, Wilcox A, Randall N (2013) A perceived gap between invasive species research and stakeholder priorities. NeoBiota 19: 67-82. https://doi.org/10.3897/neobiota.19.4897

Stakeholder priorities. (doi: 10.3897/neobiota.19.4897.app1) File format: Micrisoft Comma Separated Value File (csv).:

opencc-by-4.0Jul 2024View details →

Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city

This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m.  Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals;  the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling.  Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet. Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.

opencc-by-4.0Apr 2023View details →

Data from: Maximizing the potential for living cell banks to contribute to global conservation priorities

Summary This dataset accompanies the publication "Maximizing the potential for living cell banks to contribute to global conservation priorities" published in Zoo Biology. This study analyzed the representation of amphibian, bird, mammal and reptile species within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo® living cell collection (as of April 2019) and implemented a qualitative framework for the prioritization of species for future sampling. We used global conservation assessment schemes (including the IUCN Red List of Threatened SpeciesTM, CITES, the Alliance for Zero Extinction, the EDGE of Existence, and Climate Change Vulnerability), and opportunities for sample acquisition from the global zoo and aquarium community, to identify priority species for future cryobanking efforts. Two datasets accompany this publication. The first provides a complete list of all “Threatened” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members (Supporting Information Data S1). The second provides a list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “Threatened” under the IUCN Red List, but assessed by Foden et al. (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members (Supporting Information Data S2). Data provided by SDZWA are only available upon request. Data for each conservation assessment scheme were collected from each source individually (all freely available), and are accurate to April 2019. Zoo species holdings data were provided upon request by Species360 (https://www.species360.org/), which operates the real-time database ZIMS (Zoological Information Management System). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each species and their current population sizes.    Description of the Datasets   Supporting Information Data S1 (accurate to April 2019): A complete list of all “Threatened” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include: <ul> <li>Class: The taxonomic class of the species</li> <li>IUCN Name: The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li>IUCN Status: The IUCN Red List status of the species (VU, EN, or CR)</li> <li>ZIMS: The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> <li>AZE: The listing of the species on the Alliance for Zero Extinction assessment (listed = 1, not listed = 0)</li> <li>AZA: The presence of an active ex situ management programme for the species in the Association of Zoos and Aquariums (active programme = 1, no programme = 0)</li> <li>EAZA: The presence of an active ex situ management programme for the species in the European Association of Zoos and Aquaria (active programme = 1, no programme = 0)</li> <li>CITES: The listing of the species on of the three CITES appendices (listed = 1, not listed = 0)</li> <li>CC Vulnerability: The listing of the species as being highly vulnerable to climate change by Foden et al. 2013 (vulnerable = 1, not vulnerable = 0)</li> <li>EDGE: The listing of the species on the Evolutionary Distinctiveness and Global Endangerment assessment (listed = 1, not listed = 0)</li> </ul>   Supporting Information Data S2 (accurate to April 2019): A list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “Threatened” under the IUCN Red List, but assessed by Foden et al. (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include: <ul> <li>Class: The taxonomic class of the species</li> <li>IUCN Name: The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li>CC Vulnerability: The listing of the species as being highly vulnerable to climate change by Foden et al. 2013 (all species are vulnerable = H)</li> <li>IUCN Status: The IUCN Red List status of the species (excluding VU, EN, or CR)</li> <li>ZIMS: The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> </ul>   Acknowledgements We sincerely thank the San Diego Zoo Wildlife Alliance and Species360 member institutions for their support and data input. This research was funded by the Irish Fulbright Commission 2018/2019 to A.M., the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. D.A.C. and J.S. were funded by Species360, the University of Southern Denmark and the Species360 Conservation Science Alliance sponsors: the World Association of Zoos and Aquariums (WAZA), Mandai Wildlife Reserve, and Copenhagen Zoo.    Disclaimer Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data collected from the various conservation assessment schemes, and those contained within ZIMS, could contain errors or reporting failures. For example, ZIMS data are are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA).    Credit If you use this dataset, please cite the corresponding publication: Mooney, A., Ryder, O. A., Houck, M. L., Staerk, J., Conde, D. A., & Buckley, Y. M. (2023). Maximizing the potential for living cell banks to contribute to global conservation priorities. Zoo Biology, 1– 12. <a href="https://doi.org/10.1002/zoo.21787">https://doi.org/10.1002/zoo.21787</a>

opencc-by-4.0Sep 2023View details →

Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries

Panzeri D.1, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović &Scaron;ifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S2. 1 dpanzeri@ogs.it 2 slibralato@ogs.it Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures. Data (csv) for Panzeri et al. 2023 1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values  (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a>  2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a> 3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a>

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations

This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations

This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

opencc-by-4.0Feb 2017View details →

High-Priority Promiscuity Cliffs from PubChem

A representative sample of 278 high-priority promiscuity cliffs identified from PubChem is provided. These promiscuity cliffs were formed by pairs of structurally analogous compounds with a difference in promiscuity degrees of at last 20 targets. These compounds were tested in at least 300 shared primary assays (SA) and showed assay similarity (AS) and assay frequency ratio (AFR) values of at least 0.85. A README file is also given.

opencc-by-4.0Jan 2015View details →

Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.

Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited. Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026 The following files are contained in this folder: dataExp1.mat contains the data of experiment 1 The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right. dataExp2.mat contains the data of experiment 2 Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively. Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation) Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period. figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper

opencc-zeroOct 2023View details →

NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in American Samoa, 2023-06-06 to 2023-08-07

Description:NOAA's Coral Reef Conservation Program (CRCP) has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters depth) surrounding American Samoa. The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts.To meet this need, NOAA's National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants' inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of nine participant groups entered their mapping priorities into the online tool. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in American Samoa.Purpose:The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coast of the American Samoa. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.Methods:An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool the study area was divided into 160 hexagonal grid cells 2.6 km2 in size. Existing relevant spatial datasets (e.g., bathymetry layers, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each participant was provided with 50 virtual coins to place into grid cells that they wished to prioritize. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 5 coins could be placed into an individual grid cell. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported requirements of data were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km2 grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Hile et al. 2023, in prep.

opencc-by-4.0Sep 2023View details →

Priority landscapes for tree-based restoration in Rwanda

These priority maps highlight landscapes where the promotion of tree-based restoration practices is expected to yield higher benefits compared to possible interventions in non-priority landscapes. It is important to note that the priority maps created should not be considered as final, but as part of the process in identifying intervention areas for tree planting in Rwanda. Key further steps in prioritization include stakeholder consultations to incorporate their perspecives and field observations to further define the most adequate interventions. For more information on the methodology please consult the following documents:  Pedercini, F., Dawson, I.K., Kindt, R., Tadesse, W., Moestrup, S., Abiyu, A., Lillesø, J.P.B., Van Schoubroeck, F., McMullin, S., Carsan, S. and Mausch, K., 2021. Priority landscapes for tree-based restoration in Ethiopia. In ICRAF Working Paper. World Agroforestry Centre. [https://dx.doi.org/10.5716/WP21037.PDF; https://patspo.shinyapps.io/Restoration_Ethiopia/] Pedercini, F., Kindt, R., Dawson, I., Lillesø, JPB., Mukuralinda, A., Ndayambaje, J. D., Jamnadass, R., Graudal, L. (2023). Priority landscapes for tree-based restoration in Rwanda: a spatially explicit approach to prioritize areas for intervention. TREPA report. Pedercini, F., Kindt, R., Graudal, L. (2024). Priority landscapes for tree-based restoration in Rwanda: a spatially explicit approach to prioritize areas for intervention. World Bank report.

opencc-by-4.0Dec 2023View details →

PSM-AP Comparative document analysis data: Priorities and challenges in the policy and digital strategies of ten PSM

The document consists of a list of 61 key policy and strategy documents analysed as part of the comparative work conducted in WP1 of the project Public Service Media in the Age of Platforms (PSM-AP). It also contains a series of selected quotes supporting the three key areas prioritised by the policies and PSM digital strategies: People (reaching audiences), Personalisation (developing the video-on-demand portal), and Prominence (of PSM services and content). The data was collected and analysed in 2023, from documents concerning 10 PSM organisations in seven media markets: Belgium-Flanders (VRT), Belgium-Wallonia Brussels (RTBF), Canada (CBC/Radio-Canada), Denmark (DR, TV 2) Italy (RAI), Poland (TVP), and the UK (BBC, Channel 4, ITV). All quotes were translated to English by the authors.