Find research datasets worth reusing

opencc-by-nc-4.0Aug 2024View details →

Data Files for Climate-based Maize Loss Rate Simulations

This archive contains data files from an <a href="../records/13356711">open source pipeline</a> looking at how crop insurance rates may change in the future within the US Corn Belt using <a href="https://www.sciencedirect.com/science/article/pii/S0034425715001637">SCYM</a> and <a href="https://www.chc.ucsb.edu/data/chc-cmip6">CHC-CMIP6</a>. These are available under a Creative Commons license. Unless otherwise specified, these report on SSP245. See README for more details including column-level description of each resource. Funded by the <a href="https://dse.berkeley.edu/">Eric and Wendy Schmidt Center for Data Science and Environment</a> at the University of California, Berkeley.

opencc-by-4.0Dec 2021View details →

Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"

Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science See file ReadMe.txt for a description of the content and the original publication for further explanations. You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication. For further questions, contact sabine.rumpf@unibas.ch

opencc-by-4.0May 2022View details →

Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies

The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.

opencc-by-4.0Jun 2022View details →

Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts

This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article:  McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. Geophysical Research Letters, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a>   Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'. All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are: <ul> <li>ua:   zonal winds</li> <li>zg:   geopotential height</li> <li>ts:   surface temperature</li> <li>hfls, hfss, rlds, rlus:   surface heatfluxes</li> <li>Flat, Fz, divF:   Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc.  Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files.

opencc-by-4.0May 2021View details →

Replication data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"

Model code and predictor data underlying the publication "Climate change may induce connectivity loss and mountaintop extinction in Central American forests".

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022

This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022

This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data

This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022

This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022

This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.

opencc-by-4.0Jun 2020View details →

Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"

This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a> Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. Geophysical Research Letters, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a> <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a>

openmit-licenseOct 2020View details →

Data from: "Inventory of Earth's Ice Loss and Associated Energy Uptake from 1979 to 2017"

Earth’s cryosphere is a buffer to the warming of the planet and its loss must be accounted for in planetary energy budgets. Yet, even as melting ice is an evident manifestation of climate change, inventories of its energy uptake are largely lacking, based on inconsistent methods, or limited to the fraction that contributes to sea level rise. By combining recent syntheses, we undertake a systematic estimate of ice loss to show that Earth lost 40700 ± 5800 Gt of ice with a corresponding energy uptake of 13.8 ± 2.0 ZJ, from 1979 to 2017, larger than previous estimates and equivalent to the energy uptake by the deep ocean, the land and the atmosphere. The total loss is due to approximately equal contributions from Arctic sea-ice, the Antarctic and Greenland ice sheets, and glaciers. Only half of it contributed to sea level rise. From the 1980s to the 2010s, the rate of ice loss has almost tripled. In this HDF5 dataset, we provide cumulative annual estimates of energy uptake for three components of the cryosphere in Zetajoules (1021 Joules): 1) Antarctica 2) Greenland 2) Glaciers 3) Sea Ice For 1–3, we separate energy uptake contributions for the grounded and floating components. We also provide a Matlab file with code to read the fields in the dataset. Python code to read the data is available at: <a href="https://github.com/sioglaciology/energy_imbalance_cryosphere">https://github.com/sioglaciology/energy_imbalance_cryosphere</a>

opencc-by-4.0Oct 2024View details →

CLDF dataset with data and supplements for Barlow "Loss of colexification of 'hand' and 'five' in Austronesian languages"

opencc-by-4.0Dec 2023View details →

Data and Code for "Why are generalists the 'winners' of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes"

Data and R-based workflow for the study "Why are generalists the ‘winners’ of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes".

opencc-by-4.0Nov 2024View details →

Supporting Data for "Impacts of Antarctic ice mass loss on New Zealand climate"

Contains the model output necessary to reproduce the results of "Impacts of Antarctic ice mass loss on New Zealand climate" by Andrew G. Pauling, Inga J. Smith, Jeff K. Ridley, T. Martin, M. Thomas and D. P. Stevens. Submitted for publication to Geophysical Research Letters. Please use the "getdata.sh" script in the Github repository here: LINK to download and extract the data into the correct location for the notebooks to reproduce the results of the paper.

opencc-by-4.0Nov 2021View details →

Data for Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. DOI:10.1093/jmammal/gyab153

Data sets used in Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. (https://doi.org/10.1093/jmammal/gyab153) File Descriptions: CapHistTagLoss.txt - Capture histories for Carollia perspicillata identifying if individual was captured with both tags (B), arm bands (A), collar (C), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index. CaptHistTagInj.txt - Capture histories for Carollia perspicillata identifying if individual was captured with no lesions from arm band (A), minor injury (I), major injury (M), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index. LesionOccurrence.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of lesions of any type due to arm bands. RingCondition.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of damage to arm bands. SMI.txt - Longitudinal data for individual Carollia perspicillata Scaled Mass Index, identifying individual records, the occurrence of lesions, sex, month, year

opencc-by-4.0Dec 2021View details →

"Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data

This dataset and code is part of the following publication: Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7   There are three zip-folders accompanying this publication: Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors. Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository. TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.

opencc-by-4.0Feb 2022View details →

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19" cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19". These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19". 40-lifetables.csv Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided. 30-lt_input.csv Life table input data. <ul> <li>`id`: unique row identifier</li> <li>`region_iso`: iso3166-2 region codes</li> <li>`sex`: Male, Female, Total</li> <li>`year`: iso year</li> <li>`age_start`: start of age group</li> <li>`age_width`: width of age group, Inf for age_start 100, otherwise 1</li> <li>`nweeks_year`: number of weeks in that year, 52 or 53</li> <li>`death_total`: number of deaths by any cause</li> <li>`population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)</li> <li>`death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)</li> <li>`death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_source`: source of the all-cause death data</li> <li> `death_total_prop_q1`: observed proportion of deaths in first quarter of year </li> <li> `death_total_prop_q2`: observed proportion of deaths in second quarter of year </li> <li> `death_total_prop_q3`: observed proportion of deaths in third quarter of year </li> <li> `death_total_prop_q4`: observed proportion of deaths in fourth quarter of year </li> <li> `death_expected_prop_q1`: expected proportion of deaths in first quarter of year </li> <li> `death_expected_prop_q2`: expected proportion of deaths in second quarter of year </li> <li> `death_expected_prop_q3`: expected proportion of deaths in third quarter of year </li> <li> `death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year </li> <li>`population_midyear`: midyear population (July 1st)</li> <li>`population_source`: source of the population count/exposure data</li> <li>`death_covid`: number of deaths due to covid</li> <li>`death_covid_date`: number of deaths due to covid as of <date></li> <li>`death_covid_nageraw`: the number of age groups in the covid input data</li> <li>`ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year</li> <li>`ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database</li> <li>`nmx_hmd_estimate`: death rate estimates from the Human Mortality Database</li> <li>`nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019</li> </ul> Deaths <ul> <li>source: <ul> <li>STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)</li> <li>ONS for GB-EAW pre 2020</li> <li>CDC for US pre 2020</li> </ul> </li> <li>STMF: <ul> <li>harmonized to single ages via pclm</li> <li>pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110</li> <li>smoothing parameters estimated via BIC grid search seperately for every pclm iteration</li> <li>last age group set to [110,111)</li> <li>ages 100:110+ are then summed into 100+ to be consistent with mid-year population information</li> <li>deaths in unknown weeks are considered; deaths in unknown ages are not considered</li> </ul> </li> <li>ONS: <ul> <li>data already in single ages</li> <li>ages 100:105+ are summed into 100+ to be consistent with mid-year population information</li> <li>PCLM smoothing applied to for consistency reasons</li> </ul> </li> <li>CDC: <ul> <li>The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data</li> </ul> </li> </ul> Population <ul> <li>source: <ul> <li>for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019</li> <li>for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100</li> </ul> </li> <li>mid-year population <ul> <li>mid-year population translated into exposures: <ul> <li>if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates</li> <li>if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.</li> </ul> </li> </ul> </li> </ul> COVID deaths <ul> <li>source: COVerAGE-DB (https://osf.io/mpwjq/)</li> <li>the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total</li> </ul> External life expectancy estimates <ul> <li>source: <ul> <li>World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019</li> <li>Human Mortality Database (https://mortality.org/), single year and age tables</li> </ul> </li> </ul>

opencc-by-4.0Feb 2022View details →

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19" cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19". These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19". 50-e0diffT.csv Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019. 51-arriagaT.csv Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age. 52-sexdiff.csv Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes. 53-e0diffcodT.csv Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued. 55-vaxe0.csv Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE. 54-tab_arriaga.csv Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.