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zenodo52/100

A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)

This is the supplemental material for: Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice.  Purpose: This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions.  Available Files: Table A1: Metadata for Pb records -- Individual sample sites Table A2: Metadata for Pb records -- Transect sample sites Table A3: Records grouped into 23 regions Supplement_fig_25Aug2024: Additional figures supporting main manuscript Supplement_method_25Aug2024: Methodology used for the systematic literature review Supplement_citations_25Aug2024: Citations for all records included in the systematic literature review citations_export.bib: Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc) indexedReferences.csv: CSV dump of citations_export.bib indexed with citation keys used in TableA.3 tables.RDS: TableA.1, TableA.2, and indexed References formatted for easy import into R tables.sqlite: TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite readme_tables_sqlite.md: Examples of SQLite queries   Systematic Literature Review Methodology: To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria. <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow. </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> Records of 210Pb in snow and ice were excluded. 210Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of 210Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of 210Pb in snow and ice were excluded. </li> <li> Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature. </li> <li> The publication must be available to the author (e.g., through the University Library, from collaborators) </li> </ol> </li> </ol> To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science. Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions. Methodology Bibliography: Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage. Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312 Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005. RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz

opencc-by-4.0Sep 2022View details →

zenodo52/100

Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan

README Grigoriev data Overview The Grigoriev data collection consists of the following files, which are briefly explained further below. From a relatively large number of files and for clarity, we provide mainly those files which have been directly used in the generation of figures contained in Machguth et al. (2024). While the use in figure creation was the main selection criteria, the files have not been truncated to data shown in the figures.  The files contain more information than shown in the figures. A few files have been added for completeness although not used to create figures (see below). The data sets provided in this repository are listed in the following. Most of these tables contain relatively raw data.  The suggested citations are added in brackets. Please also check Table 1 in Machguth et al. (2024) for potential further references. - 1990_GRG_90_H1-BETA.xlsx                                          (Arkhipov et al., 1996; Thompson et al., 1997) - 1990_GRG_90_H1-CHM.xlsx                                           (Arkhipov et al., 1996; Thompson et al., 1997) - 1990_GRG_90_H1-STRAT.xlsx                                            (Arkhipov et al., 1996; Thompson et al., 1997) - 1990_GRG_90_H2-CHM.xlsx                                            (Arkhipov et al., 1996; Thompson et al., 1997) - 1990_GRG_90_H2-STRAT.xlsx                                            (Arkhipov et al., 1996; Thompson et al., 1997) - 1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx                            (Arkhipov et al., 1996; Thompson et al., 1997; Machguth et al., 2024) - 2001_GRG_01_S1-EE.xlsx                                            (Arkhipov et al., 2004; Mikhalenko et al., 2005) - 2001_metals.pdf                                                    (Usubaliev, 2003) - 2003_GRG03-S1-EE_001.xlsx                                            (Mikhalenko et al., 2005; Kutuzov, 2005) - 2003_GRG03-S2-EE 001.xlsx                                            (Mikhalenko et al., 2005; Kutuzov, 2005) - 2003_pits.xlsx                                                    (Mikhalenko et al., 2005; Kutuzov, 2005) - 2003_temperature_density.xlsx                                        (Mikhalenko et al., 2005; Kutuzov, 2005) - 2003_temperature_logger_data.xls                                    (Mikhalenko et al., 2005; Kutuzov, 2005) - 2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx        (Machguth et al., 2024) - 2018_PSI_dating_20230517.xlsx                                        (Eichler et al., 2020; Machguth et al., 2024) Detailed Information 1990_GRG_90_H1-BETA.xlsx: refers to Core 1 1990 (labelled H1 probably for "Hole 1"). Unknown to what the 1991 data refer, likely a repeat measurement. 1990_GRG_90_H1-CHM.xlsx: Chemistry Core 1 1990. 1990_GRG_90_H1-STRAT.xlsx: Stratigraphic information Core 1 1990 1990_GRG_90_H2-CHM.xlsx: Chemistry Core 2 1990 1990_GRG_90_H2-STRAT.xlsx: Stratigraphic information Core 2 1990 1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx: This table we calculated from the 1990 tables as well as the 2018 data for the purpose of visualizing                                            decadal means in MIs (Fig. 7). Decadal dating of the 1990 cores was done based on the bomb horizon of 1963 (Thompson et al., 1993, 1997),                                             decadal picks from Thompson et al. (1993) and personal communication by Lonnie Thompson (email 19 June 2023). 2001_GRG_01_S1-EE.xlsx: 2001 core, stable water isotope ratios, firn temperatures, percentage of infiltration ice, stratigraphy 2003_GRG03-S1-EE_001.xlsx: 2003 51m and 22.6m cores, 51m core was drilled thermally, 22.6m core mechanically. For the latter similar data as for 2001 2003_GRG03-S2-EE 001.xlsx: 2003 21.3m core. Reduced amount of measured parameters compared to e.g. 2001 core.  2003_pits.xlsx: Stratigraphy and density measured in a series of snow pits in 2003. 2003_temperature_density.xlsx: Density and temperature measured in 2003 22.6m core. Comparison of T_ice at 4440 m a.s.l. to 1962 core (Dikikh, 1965) 2003_temperature_logger_data.xls: Firn temperatures measured through a thermistor chain during 3 days in June 2003. Data from 14 June have been used for Fig. 5. 2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx: 2018 core stratigraphy and density. This is a somwhat outdated file which shows the data per centimetre.                                                             The file compares the two measurements of density (only the one from the laboratory was used in Machguth et al., 2024).                                                              Also contains visually observed dust layers (not shown in Machguth et al., 2024) 2018_PSI_dating_20230517.xlsx: Complete data from the analysis of the 2018 core. Bibliography Arkhipov, S. M., Mikhalenko, V. N., & Thompson, L. (1996). Struktura i stratigrafiya deyatel’nogo sloya lednika Grigor’eva na Tyan’-Shanye (Structure and stratigraphy of the active layer  of the Griroriev glacier in the Tjan-Shan). Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 80, 68–83. Arkhipov, S. M., Mikhalenko, V. N., Kunakhovich, M. G., Dikikh, A. N., and Nagornov, O. V.: Termicheskiy reshim, uslovija l’doobrazovanija i akkumulatsija na lednike Grigor’eva (Tyan’- Shan), v 1962–2001 gg. (Thermal regime, types of ice formation and accumulation on the Grigoriev glacier (Tien Shan), 1962–2001), Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 96, 77–83, 2004. Eichler, A., Kronenberg, M., Brütsch, S., Rüthi, M., Heule, M., Schwikowski, M., et al. (2020). Chernobyl horizon in a Central Asian ice core.  Annual Report 2019 - Laboratory of Environmental Chemistry - PSI, 31. Kutuzov, S. S.: Prostranstvennie izmenenija i stroenie lednikov vnutrennogo Tyan’-Shanya za poslednie 150 let (Spatial changes and structure of the glaciers of the inner Tien Shan over the last 150 years), Master’s thesis, Lomonossov State University, Moskva, 2005. Machguth, H., Eichler, A., Schwikowski, M., Brütsch, S., Mattea, E., Kutuzov, S., et al. (2024). Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan.  The Cryosphere, 18(4), 1633–1646. https://doi.org/10.5194/tc-18-1633-2024 Mikhalenko, V. N., Kutuzov, S. S., Fayzrakhmanov, F. F., Nagornov, . B., Thompson, L. G., Kunakhovich, M. G., Arkhipov, S. M., Dikikh, A. N., and Usubaliev, R.: Sokrashhenie oledenenija Tyan’-Shanja v XIX – nachale XXI vv.: rezul’taty kernovoro burenija i izmerenija temperatury v skvazhinakh (Glacier recession in the Tien Shan from the XIX to the beginning of the XXI century: results from ice core drilling and borehole temperature measurements), Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 98, 175–182, 2005. Thompson, L. G., Mosley-Thompson, E., Davis, M., Lin, P. N., Yao, T., Dyurgerov, M., & Dal, J. (1993). “Recent warming” ice core evidence from tropical ice cores with emphasis  on Central Asia. Global Planet. Change, 7(1–3), 145–156. https://doi.org/10.1016/0921-8181(93)90046-Q Thompson, L. G., Mikhalenko, V., Mosley-Thompson, E., Durgerov, M., Lin, P. N., Moskalevsky, M., et al. (1997). Ice core records of recent climatic variability: Grigoriev and It-Tish ice caps  in Central Tien Shan, Central Asia. Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 81, 100–109. Usubaliev, R. A. (2003). Khimitcheskoe zagryaznenie lednikov Tyan’-Shanya (na primere lednika Grigor’eva) (Chemical pollution of Tien Shan glaciers (on the example of Grigoriev Glacier)).  Izvestija Natsional’noy Akademii Nauk Kirgizskoy Respubliki (News of the National Academy of Sciences of the Kyrgyz Republic), 4, 154–160.

opencc-by-4.0Oct 2024View details →

zenodo52/100

Early EASE-GRID Sea Ice Age, 1978-1983

Early spin-up period Arctic sea ice age data for 1978 through 1983. This product augments the NSIDC sea ice age product: "EASE-Grid Sea Ice Age, Version 4.1" (Tschudi et al., 2019a), which begins in January 1984. See the main product website for complete documentation. The age is estimated via Lagrangrian tracking based on the NSIDC sea ice motion product (Tschudi et al., 2019b), whose source data is primarily passive microwave brightness temperatures and drifting buoys. Age is estimated weekly as annual age categories. Values are: 1 for "first-year ice", ice that is 0-1 years old, and so on for older ice. The ice is "aged" once each year during the week of the annual sea ice minimum extent, generally sometime in September.  In this product, the initialization of the field begins with the first available data in late-October 1978. For the existing ice at that time, age is initialized at the start of the product with age=1. The first week of the data, because it is after the minimum, the age of existing ice is augmented to age=2 and new ice is given age=1. So, the first field in 1978 has only two age categories of 1 (0-1 years old) or 2 (1-2 years old) and this continues through 1978. This means that the age of the ice that formed between the minimum in September and the beginning of the data in late-October 1978 is overestimated by one year. In subsequent years, the oldest ice category will continue to overestimate some of the ice pack until that initial ice either: (1) melts, (2) is transported out of the Arctic, or (3) reaches the maximum age in the product (16 years). Much of the the existing ice in 1978 may be older than 1-2 years old as ice may stay in the Arctic for 5 or more years, but the data availability and the Lagrangian methodology cannot give a specific until the product is fully "spun up". For each subsequent year, a one-year older age category is added in the week of each year's extent minimum. Note that due to the assumption made at the beginning of the product in 1978, the oldest ice category may overestimate the true age of some parcels by one year.  Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019a). EASE-Grid Sea Ice Age, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/UTAV7490FEPB. Date Accessed 02-20-2023. Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019b). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/INAWUWO7QH7B.

opencc-by-4.0Feb 2023View details →

edi52/100

Journey North - Common Loon and Ice-Out observations by volunteer community scientists across North America (1997-2020)

This data package contains Common Loon migration and ice melt data consisting of 9,800 total observational reports from 1997 - 2020 across North America. These data were collected by 1,437 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Loon and Ice-Out Project is an ongoing study of loon and ice melt phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked first arrival dates of Common Loons (Gavia immer) and ice that has melted from bodies of water in the United States. Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Common Loon and Ice-Out Project Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.

openCC (other)Aug 2022View details →

edi52/100

Ice timing (formation or ice-on and clearance or ice-off) for Yellowstone Lake, Wyoming, USA (1927-2022)

Lakes are sentinels of environmental change. In cold climates, lake ice phenology–the timing and duration of ice cover during winter–is a key control on ecosystem function. Ice phenology appears to be driven by a complex interplay between physical characteristics and climatic conditions. Under climate change, lakes are generally freezing later, melting out earlier, and experiencing a shorter duration of ice cover; however, few long-term records exist for large, high-elevation lakes which may be particularly vulnerable to climate impacts. Here, we provide an ice phenology data over the last century (1927-2022) for North America’s largest high-elevation lake—Yellowstone Lake.

openCC (other)Apr 2024View details →

edi52/100

IISD Experimental Lakes Area: Ice Phenology and Thickness, 1969-2025 (partial start/end years).

The IISD Experimental Lakes Area (IISD-ELA) Ice Phenology and Thickness data package provides two types of ice data from multiple lakes in northwestern Ontario, Canada. Ice phenology is the timing of when lakes freeze over in the fall and thaw out in the spring, as an ice-on and ice-off date each year, and the days of ice duration for each winter. Ice thickness data includes total ice thickness as well as the complex breakdown of individual layers (snow, slush, white ice, black ice). This data package includes both tabular data and metadata files for each type of ice data. The ice phenology table consists of a row for each ice-on date, ice-off date, and ice duration period for each lake, including the associated sampling method and any comments. The ice thickness table has a row for each measurement of a frozen lake on a specific date, including the total ice thickness and the thickness of individual layers as column values for each row. Metadata in this data package include a table of location coordinates and record counts, and an information sheet for ice phenology and one for ice thickness. The table of coordinates and counts is useful to know where the lakes and specific sampling sites are located and as an overview of data availability per lake (Lake 239 has the longest and most consistent data record, for both ice phenology and thickness). The two info sheets provide additional metadata details about the datasets, including background and uses of the datasets, a data dictionary, diagrams, lookup tables, descriptions of methods, and additional references. For data about lake depth, size, and volume, please consult the most recent version of our bathymetry data package: https://portal.edirepository.org/nis/revisionbrowse?scope=edi&identifier=1276 This data package is ongoing—updates will be provided as data are collected from these lakes in subsequent years. If data are not present for a lake you are interested in from IISD-ELA, please get in touch with us. The

openCC (other)Aug 2025View details →

edi52/100

Meteorological data collected on Lake E5 during the ice free season since 2000 to present, Arctic LTER, Toolik Research Station, Alaska.

Yearly file describing the metological data on Lake E5 (Lake E5 Climate station) near the Toolik Field Research Station (68 38'N, 149 36'W). Measurements include air temperature, relative humidity, wind direction, and wind speed..

openCC (other)Jan 2016View details →

edi52/100

Hubbard Brook Experimental Forest: Mirror Lake Ice Cover 1968 - ongoing

This data set reports ice on and ice off dates for Mirror Lake beginning in 1968 and continuing through the present. Mirror Lake is located within the Hubbard Brook valley in the White Mountains of New Hampshire, and has been the subject of numerous limnological investigations since the early 1960s. These Mirror Lake data are part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), a long-term record of weekly sampling of nine gaged watersheds at Hubbard Brook which includes the stream draining Mirror Lake. The collection and management of the long-term record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emily Bernhardt (Duke), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Keith Nislow (USFS), and Mark Green (Case Western). Current financial Support for HBWatER is provided by NSF LTREB # 2401760 and the USDA Forest Service Northern Research Station. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Apr 2025View details →

edi52/100

Lake ice seasonality over the past 320-570 years

Lake and river ice seasonality (dates of ice freeze and breakup) responds sensitively to climatic change and variability. We analyzed climate-related changes using direct human observations of ice freeze dates (1443-2014) for Lake Suwa, Japan, and of ice breakup dates (1693-2013) for Torne River, Finland. We found a rich array of changes in ice seasonality of two inland waters from geographically distant regions: namely a shift towards later ice formation for Suwa and earlier spring melt for Torne, increasing frequencies of years with warm extremes, changing inter-annual variability, waning of dominant inter-decadal quasi-periodic dynamics, and stronger correlations of ice seasonality with atmospheric CO2 concentration and air temperature after the start of the Industrial Revolution. Although local factors, including human population growth, land use change, and water management influence Suwa and Torne, the general patterns of ice seasonality are similar for both systems, suggesting that global processes including climate change and variability are driving the long-term changes in ice seasonality.

openCC (other)Dec 2022View details →

edi52/100

Lake Mendota water temperature secchi depth snow depth ice thickness and meterological conditions 1894 - 2007

Data for water temperature at different depth and different frequencies assembled from various sources by Dale Roberson. A table with additional parameters collected at the same time is also provided for dates when available. These parameters are weather observations, secchi depth, snow and ice depths.

openCC (other)Dec 2022View details →

edi52/100

Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.

Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.

openCC (other)Aug 2024View details →

edi52/100

Average monthly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1978 - June 2024.

Monthly sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including (1) monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines); and (2) monthly sea-ice concentration (%) extracted for small PAL subregions, including the nominal penguin foraging areas (~200km by ~200km) southwest of King George Island (KGI), Anvers, Avian and Charcot islands, as well as for Marguerite Bay (~140km by ~140km) (inland of the area defined for Avian).

openCC (other)Aug 2024View details →

edi52/100

Average yearly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1979 - 2023.

Annual sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including: monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines).

openCC (other)Aug 2024View details →

zenodo48/100

Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"

This dataset is used to reproduce the results presented in the following publication: Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.

opencc-by-4.0Apr 2020View details →

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Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada

Data and computer code for producing figures for the manuscript: Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations  in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave  Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020.  Discussion paper and final version: https://doi.org/10.5194/tc-2020-33   ==========================================================================================    CONTENT OF DIRECTORIES ========================================================================================== -– data [input data to produce plots]    |–– BoreholesMeta.csv    |–– brackets_photos_ice.csv    |–– brackets_photos_thawed.csv    |–– Lac_de_Gras_permafrost_20200612.csv    |–– NordicanaD        |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]        |–– Cored_Drill_TCR.csv        |–– Cored_Drill_TCR.csv_ReadMe.txt            |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]        |–– Cored_Drill_Logs.csv_ReadMe.txt        |–– Cored_Drill_Logs.csv –– plot [R scripts write plots into this subdirectory] –– src [R scripts to generate plots]    |–– Combined_Plots.R              [produces Figures 3–6]    |–– Eskers.R                      [helper function called by Combined_Plots.R]    |–– Organics.R                    [helper function called by Combined_Plots.R]    |–– plot_boreholes_DD_single.R    [produces Figures S3]    |–– plot_boreholes_DD.R           [produces raw Figure S2 for further graphic processing]    |–– Till.R                        [helper function called by Combined_Plots.R]    |–– Valley.R                      [helper function called by Combined_Plots.R] ==========================================================================================    RUNNING SCRIPTS ========================================================================================== Adjust the variable 'path' in these scrips, then run:      Combined_Plots.R     plot_boreholes_DD_single.R     plot_boreholes_DD.R  Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"   ==========================================================================================    REFRERENCE ========================================================================================== Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018) and only included here for convenience. The full reference for the authoritative copy is:         Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C.,  Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015  surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories,  Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6.   http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6

opencc-by-4.0Jan 2020View details →

zenodo48/100

Ice extent in Norwegian fjords, 2001-2019

Ice extent in Norwegian fjords between 2001 to 2019 derived using MODIS imagery. The associated polygons used to outline each fjord/coastal area are provided in the '01_polygons.txt'. Data is presented and analyzed further in: O’Sadnick M, Petrich C,, Brekke C., Skarðhamar J (2020). Ice extent in sub-arctic fjords and coastal areas from 2001 to 2019 analyzed from MODIS imagery. Annals of Glaciology 1–17. https://doi.org/10.1017/aog.2020.34 In addition, an interactive map can be found at: https://ndat.no/fjords/ice/

opencc-by-4.0Oct 2020View details →

zenodo48/100

Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output

Model output used in the publication: M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. Proc. Natl. Acad. Sci. (2021) https://doi.org/10.1073/pnas.207541118 See README.md for a description of the data.

opencc-by-4.0Nov 2020View details →

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ICESat-2 monthly gridded winter Arctic sea ice thickness

Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below. Temporal range: November 2018 - April 2019, October 2019 to April 2020. Data: A single netCDF file is included for each month. Variables include: <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication. Key changes from version 1 (Petty et al., 2020) to version 2 include: <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: gs://sea-ice-thickness-data/v2/

opencc-by-4.0Nov 2020View details →

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Ice Nucleating Particle number concentration from low-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods. Dataset contents <ul> <li>ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>data_file_header_frozen_fraction.txt, metadata, text format</li> <li>data_file_header_number_concentration.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> Change log v1.1 - data files updated <ul> <li>change dataset title to reflect low-volume sampling method</li> <li>addition of INP number concentration data from different temperatures</li> <li>addition of fraction of frozen droplets data</li> <li>addition of field blank filter data</li> <li>create separate data_file_header files</li> <li>add change log</li> </ul> v1.0 - initial release of dataset

opencc-by-4.0Dec 2020View details →

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Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022

File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.

opencc-by-4.0Nov 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record