Find research datasets worth reusing

openCC0Feb 2025View details →

Lower American River dissolved oxygen monitoring, CA (2022 - 2024)

The purpose of the Lower American River (LAR) dissolved oxygen monitoring is to provide timely information to better understand how dissolved oxygen levels in the LAR fluctuate over time and space, particularly in relation to Folsom and Nimbus Dam operations, and if conditions are stressful to adult Chinook salmon in Nimbus Basin during the fall spawning period. This information has been collected from summer 2022 to present (winter 2024) used to inform resource managers about a biologically important water quality parameter during a key period in Chinook Salmon life history so they can take action to address poor water quality using water releases from dam gates or other means. Reports are provided to the American River Group as needed, generally on a monthly basis, to support water quality management discussions.

openCC (other)Feb 2020View details →

American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013

"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."

openCC (other)Mar 2020View details →

American Residential Macrosystems - Complete municipal ordinance documents across six U.S. cities, 2017-2019

These data files are the complete city codes, or municipal ordinances (n=156), across the metropolitan regions of Los Angeles, CA; Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis/St. Paul, MN. The documents were gathered for the specific purposes of a content analysis of how cities regulate residential landscapes; however, the documents include regulations on the books for the municipalities sampled for this project.

openCC (other)May 2020View details →

American Residential Macrosystems - Bird community data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018

"This dataset includes abundance of breeding bird species recorded in residential yards and nearby natural and interstitial areas (i.e.unmanaged vegetation areas in the residential/wildland interface) in six cities across the U.S. Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ. Yards were grouped in 4 categories based on fertilizer input frequency, landscaping style and their impact on hydrology: high-input lawns, low-input lawns, wildlife-certified yards and yards with low impact on hydrology (or rain gardens). Bird data was collected via standardized 10-min point counts during the breeding season in 2017 or 2018. "

openCC (other)Jan 2020View details →

American alligator GPS tracking study from May 2008 to September 2010 on Sapelo Island, Georgia

We deployed GPS tracking units on seven adult American alligators (two females and five males), for periods ranging from 34 to 100 days from May 2008 to September 2010 on Sapelo Island, Georgia. GPS units were set to record the location of tracked alligators every 1.5 to 2 hours. GPS data were then downloaded and tracks analyzed using GIS software after recapture.

openCC (other)Dec 2022View details →

Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.

To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).

openCC0Mar 2021View details →

Rodent declines track regional climate variability in North American drylands

Regional long-term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space-for-time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, which cover ~45% of Earth’s land surface and face increasingly drier and more variable climates. Here, we analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland habitats in the southwestern USA. We captured two time series: 1995-2006 and 2004-2013 that coincide with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20-35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004-2013, with losses of ~5% of animals captured. During the first time series (PDO wet phase), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (dry phase), climate best explained rodent abundance for 60% of taxa. Temporal dynamics in rodent diversity and abundance differed spatially among habitats and sites, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Both habitat type and phase of the PDO modulated which species were winners or losers under increasing drought and amplified interannual variability in drought. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among habitats for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995 - 2013. Whether these changes portend future declines in drought-sensitive consumers in the southwestern USA will depend on the climate during the next major phase of the PDO.

opencc-by-4.0Mar 2024View details →

Combined RSEI Water Scores and American Community Survey, 2011–2021

This dataset was created for a master's thesis in geography at the University of Utah. County-level RSEI scores and county demographics were combined for a spatiotemporal regression analysis. Counties in the contiguous United States and years 2011–2021 are included. Risk-Screening Environmental Indicators. Annual, county-level RSEI risk scores for surface water were obtained from the EPA RSEI model through the <a href="https://edap.epa.gov/public/extensions/EasyRSEI_AllYears/EasyRSEI_AllYears.html">EasyRSEI Dashboard</a>. RSEI scores are <a>defined as the relative potential for health risks, given the estimated exposure calculated by the EPA</a>. A binary risk indicator was created, signifying whether the county had any risk in a given year. American Community Survey 5-year estimates. County-level population characteristics were collected as five-year estimates from the American Community Survey, with annual estimates for the survey end year applying to each year from 2011–2021. We used the R tidycensus package to collect variables and geometry from the US Census Bureau API. Variables included <a>total population (scaled by 10,000), percent racial composition (American Indian or Alaska Native, or AIAN; Asian; Black or African American; Native Hawaiian or Other Pacific Islander, or NHPI; Some Other Race; Two or More Races; and White), and percent ethnic composition (Hispanic or Latinx). </a>Each census-defined racial category was collected as non-Hispanic or Latinx, such that each group was mutually exclusive.

opencc-by-4.0Nov 2024View details →

Differential DNA methylation in the benign and cancerous prostate tissue of African American and European American men

The data presented here are the summary statistics for the manuscript, "Differential DNA methylation in the benign and cancerous prostate tissue of African American and European American men." The study aims to improve our understanding of prostate cancer disparities between African American and European American men by comparing the DNA methylation features that distinguish tumor and paired, histologically benign tissue from a sample of African American and European American prostate cancer patients. The summary statistics presented here represent the results of a differential methylation analyses comparing tumor and benign tissue in each ancestry group as well as the results of an analysis of differential methylation by ancestry group within each tissue.   The files included are:  AA_TumorvBenign_Dummy.zip which contains the results of the association analysis between tumor vs benign (benign as the default) tissue status and individual CpG sites in African Americans based on a model that accounts for the paired nature of samples using a series of dummy variables for individual.  AA_TumorvBenign_MixedModel.zip which which contains the results of the association analysis between tumor vs benign (benign as the default) tissue status and individual CpG sites in African Americans based on a model that accounts for the paired nature of samples using a linear mixed model that included patient as a random effect.  EA_TumorvBenign_Dummy.zip which contains the results of the association analysis between tumor vs benign (benign as the default) tissue status and individual CpG sites in European Americans based on a model that accounts for the paired nature of samples using a series of dummy variables for individual.  EA_TumorvBenign_MixedModel.zip which which contains the results of the association analysis between tumor vs benign (benign as the default) tissue status and individual CpG sites in European Americans based on a model that accounts for the paired nature of samples using a linear mixed model that included patient as a random effect.  Benign_AncestryCompare_Summary.zip which contains the results of an association analysis between ancestry designation (African American vs European American with African American as the baseline) and individual CpG sites in benign tissue.  Tumor_AncestryCompare_Summary.zip which contains the results of an association analysis between ancestry designation (African American vs European American with African American as the baseline) and individual CpG sites in tumore tissue.

opencc-by-4.0Feb 2020View details →

BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification

BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification =============================================================== Version 2.0, February 2022. https://wp.nyu.edu/birdvox Description --------------- BirdVox-ANAFCC is a dataset of short audio waveforms, each of them containing a flight call from one of 14 birds of North America: four American sparrows, one cardinal, two thrushes, and seven New World warblers. * American Tree Sparrow (ATSP) * Chipping Sparrow (CHSP) * Savannah Sparrow (SAVS) * White-throated Sparrow (WTSP) * Red-breasted Grosbeak (RBGR) * Gray-cheeked Thrush (GCTH) * Swainson's Thrush (SWTH) * American Redstart (AMRE) * Bay-breasted Warbler (BBWA) * Black-throated Blue Warbler (BTBW) * Canada Warbler (CAWA) * Common Yellowthroat (COYE) * Mourning Warbler (MOWA) * Ovenbird (OVEN) It also contains other sounds which are often confused for one of the species above. These "confounding factors" encompass flight calls from other species of birds, vocalizations from non-avian animals, as well as some machine beeps. BirdVox-ANAFCC results from an aggregation of various smaller datasets, integrated under a common taxonomy. For more details on this taxonomy, we refer the reader to [1]: [1] Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. The second version of the BirdVox-ANAFCC dataset (v2.0) contains flight calls from the BirdVox-full-night dataset. These flight calls were present in the ICASSP 2020 benchmark but did not appear in the initial release of BirdVox-ANAFCC. Data Files ------------ BirdVox-ANAFCC contains the recordings as HDF5 files, sampled at 22,050 Hz, with a single channel (mono). Each HDF5 file contains flight call vocalizations of a particular species. The name of each HDF5 file follows the format: `<data-source>_<taxonomy-code>_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording varying in format depending on the data source.   Metadata Files --------------- `taxonomy.yaml` details the three-level taxonomy structure used in this dataset, reflected in three-number-codes which largely follow "<family>.<order>.<species>". Additionally, at any level of the taxonomy, the numeric code "0" is reserved for "other" and the code "X" refers to unknown. For example, 1.1.0 corresponds to an American Sparrow with a species outside of our scope of interest, and 1.1.X corresponds to an American Sparrow of unknown species. At the top level (family), the "other" codes (0.\*.\*) deviate from the family-order-species in order to capture a variety of other out-of-scope sounds, including anthropophony, non-avian biophony, and biophony of avians outside of the scope of interest. Please acknowledge BirdVox-ANAFCC in academic research -------------------------------------------------------------------------- When BirdVox-ANAFCC is used for academic research, we would highly appreciate it if  scientific publications of works partly based on this dataset cite the following publication: Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.   Conditions of Use ---------------------- Dataset created by Aurora Cramer, Vincent Lostanlen, Bill Evans, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello.   The BirdVox-ANAFCC dataset is offered free of charge under the terms of the Creative Commons Attribution International License: https://creativecommons.org/licenses/by/4.0/   The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-ANAFCC dataset or any part of it. Feedback ------------- Please help us improve BirdVox-full-night by sending your feedback to: vincent.lostanlen@gmail.com and auroracramer@nyu.edu In case of a problem, please include as many details as possible. Versions ------------ 1.0, May 2020: initial version, paired with ICASSP 2020 publication. 2.0, February 2022: added a missing dataset file (BirdVox-70k), updated name of first author (Aurora Cramer).   Acknowledgement -------------------------- Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes. We thank contributors and maintainers of the Macaulay Library and the Xeno-Canto website. We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.

opencc-by-4.0Oct 2021View details →

High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019

Dataset overview This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework. Please cite as: Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381. This dataset is supplement to: Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations.    Abstract We present ΔO2/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O2 budget. Large positive mixing fluxes, exceeding 100 mmol O2 m-2 d-1, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O2 m-2 d-1, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O2 gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO2/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.   Lay summary Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O2) concentrations. However, while vertical mixing may be a significant component of the surface water O2 budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: Lâbradorip Imappinga), North Atlantic, Hudson Strait (Ikirasarjuaq) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (Saknirutiak Imanga) and southern CAA. Riverine freshwater input to Hudson Bay (Tasiujarjuar) and the Queen Maud Gulf (Ugjulik) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.   Acknowledgements This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada. Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval. The model simulation was run by P. Myers (University of Alberta). The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia) All data were archived by R. Izett.    Files and variables: data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities. <table> <tbody> <tr> <td>Variable</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table>   nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output.  <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table>   ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling. <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table>

opencc-by-4.0Apr 2021View details →

A Weakly-Labeled Stance Dataset during the 2019 South American Protests

Research across different disciplines has documented the expanding polarization in social media. However, much of it focused on the US political system or its culturally controversial topics. In this work, we explore polarization on Twitter in a different context, namely the protest that paralyzed several countries in the South American region in 2019. By leveraging users’ endorsement of politicians' tweets and hashtag campaigns with defined stances towards the government of each country (for or against), we construct a weakly labeled stance dataset with hundreds of thousands of users. Moreover, through the synergistic usage of network-focused methods applied on news sharing patterns and language-focused methods, we validate our labeling methodology by showing that these stances partition the users into meaningful communities. That is, we show that polarization in users' news sharing patterns was consistent with their stances towards the government and that polarization in their language mainly manifested along ideological, political, or protest-related lines.

opencc-by-4.0Feb 2022View details →

Fisheries dataset on moulting patterns and shell quality of American lobsters H. americanus in Atlantic Canada

This survey collated data on lobster moult indicators and on life-history traits (sex, size) during a twelve-year monitoring program (2004 – 2015) in six lobster fishing areas in Atlantic Canada. A standardized sampling protocol was followed to collect data from a total of 141,659 lobsters over 1,195 sampling events using commercial lobster fishing traps. Data on pleopod stages, hemolymph protein levels (˚Brix values) and shell hardness can be used for moult stage determination. Evaluation of sex ratio dynamics is also possible but existing biases in sampling males and females need to be noted. This dataset is valuable in terms of inferring spatio-temporal trends in the life history of lobsters, as well as in the analysis of their moult cycle, and hence more generally for fisheries science and marine ecology.

opencc-by-4.0Jul 2024View details →

Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018

The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements. If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details. The following data sets are included: TM5-4DVar_ACOS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together. TM5-4DVar_RT_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together. TM5-4DVar_GOSAT_MeanAcosRt_SAT: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT. TM5-4DVar_IS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements. TM5-4DVar_prior_SAT: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar. TM5-4DVar_GOSAT_MeanAcosRt_arideast: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region. TM5-4DVar_GOSAT_MeanAcosRt_aridwest: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region. TM5-4DVar_GOSAT_MeanAcosRt_humid: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region. All data sets have the following variables: MonthDate: date (YYYY-MM-DD) of the middle of the individual month Month: MM Year: YYYY NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month. NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.   Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013.  Metz, E.-M., Vardag, S.N.,  Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V.  K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K.,  Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S.,  Deutscher, N.M.,  Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.

opencc-by-4.0Aug 2024View details →

Stratigraphic Framework of the US Eastern North American Margin

The current dataset consists of seismic picks, and structural and isopach maps that depict the stratigraphic division of the sedimentary cover of the US part of the Eastern North American Margin. The stratigraphic analysis is based on tied seismic and well data spanning the continental shelf, slope and rise from Cape Hatteras to the US-Canada maritime border. In addition, it incorporates published hydrostratigraphic data that divide the coastal plain sedimentary cover. File formats are GeoTiff and Zmap+ (grids) and .CSV and .shp (points). Seismic interpretation, well analysis, stratigraphic correlation and data curation were done as part of a PhD study (Lang, 2023).

Spreadsheet template for Body Size Data for North American Orthopteroid Insects

Body size data for orthopteroid insects extracted from: Vickery, V.R., Kevan, D.K.McE., 1985. The insects and arachnids of Canada, Part 14. The Grasshoppers, Crickets, and Related Insects of Canada and Adjecent Regions. Research Branch Agriculture Canada Publication 1777:1-918.

Body Size Data for North American Spiders

Body size data for North American spiders extracted from The Insects and Arachnids of Canada: Dondale, C. D. & Redner, J. H. (1978). The insects and arachnids of Canada, Part 5. The crab spiders of Canada and Alaska, Araneae: Philodromidae and Thomisidae. Research Branch Agriculture Canada Publication 1663: 1-255. Dondale, C. D. & Redner, J. H. (1982). The insects and arachnids of Canada, Part 9. The sac spiders of Canada and Alaska, Araneae: Clubionidae and Anyphaenidae. Research Branch Agriculture Canada Publication 1724: 1-194. Dondale, C. D. & Redner, J. H. (1990). The insects and arachnids of Canada, Part 17. The wolf spiders, nurseryweb spiders, and lynx spiders of Canada and Alaska, Araneae: Lycosidae, Pisauridae, and Oxyopidae. Research Branch Agriculture Canada Publication 1856: 1-383. Platnick, N. I. & Dondale, C. D. (1992). The insects and arachnids of Canada, Part 19. The ground spiders of Canada and Alaska (Araneae: Gnaphosidae). Research Branch Agriculture Canada Publication 1875: 1-297.

Spreadsheet Template for Body Size Data for North American Hemiptera

Body size data for North American Hemiptera extracted from The Insects and Arachnids of Canada: Hamilton, K.G.A., 1982. The insects and arachnids of Canada, Part 10. The Spittlebugs of Canada. Homoptera: Cercpidae. Research Branch Agriculture Canada Publication 1740:1-102. Kelton, L.A., 1978. The insects and arachnids of Canada, Part 4. The Anthocoridae of Canada and Alaska: Heteroptera, Anthocoridae. Research Branch Agriculture Canada Publication 1639:1-101. Kelton, L.A., 1980. The insects and arachnids of Canada, Part 8. The plant bugs of the prairie provinces of Canada (Heteroptera: Miridae). Research Branch Agriculture Canada Publication 1703:1-408. Matsuda, R., 1977. The insects and arachnids of Canada, Part 3. The Aradidae of Canada: Hemiptera: Aradidae. Research Branch Agriculture Canada Publication 1634:1-116.

Spreadsheet Template for Body Length Data for North American Beetles

Body size data for North American beetles extracted from The Insects and Arachnids of Canada: Anderson, R.S., Peck, S.B., 1985. The insects and arachnids of Canada, Part 13. The Carrion Beetles of Canada and Alaska: Coleoptera: Silphidae and Agyrtidae. Research Branch Agriculture Canada Publication 1778: 1-121. Bright, D.E., 1976. The insects and arachnids of Canada, Part 2. The bark beetles of Canada and Alaska: Coleoptera: Scolytidae. Research Branch Agriculture Canada Publication 1576: 1-241.  Bright, D.E., 1987. The insects and arachnids of Canada, Part 15. The Metallic Wood-boring Beetles of Canada and Alaska. Coleoptera: Buprestidae. Research Branch Agriculture Canada Publication 1810: 1-335. Bright, D.E., 1993. The insects and arachnids of Canada, Part 21. The Weevils of Canada and Alaska: Volume 1. Coleoptera: Curculionoidea, excluding Scolytidae and Curculionidae. Research Branch Agriculture Canada Publication 1882: 1-217.