Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions.

 This project has provided a population database depicting the
 worldwide distribution of population in a 1X1 latitude/longitude grid
 system. The database is unique, firstly, in that it makes use of the
 most recent data available (1990). Secondly, it offers true
 apportionment for each grid cell that is, if a cell contains
 populations from two different countries, each is assigned a
 percentage of the grid cell area, rather than artificially assigning
 the whole cell to one or the other country (this is especially
 important for European countries). Thirdly, the database gives the
 percentage of a country's total population accounted for in each
 cell. So if a country's total in a given year around 1990 (1989 or
 1991, for example) is known, then population in each cell can be
 calculated by using the percentage given in the database with the
 assumption that the growth rate in each cell of the country is the
 same. And lastly, this dataset is easy to be updated for each country
 as new national population figures become available.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The data associated with this record is accessible via the parent record - https://data.aad.gov.au/metadata/Historical_Fish_data

This dataset contains the data from Voyage 6 1990-91 of the Aurora Australis. The observations were taken from the Prydz Bay area, Antarctica in January and February 1991. The data contains the results from pelagic fish trawl surveys. The major species were Pleuragramma antarcticum, Channich thyid, Dacodraco hunteri and Neopagetopsis ionah. This is a subset of the data for the whole voyage. The objectives of the fish program were: to assess the distribution and abundance of pelagic fish in the Prydz Bay area; to re-sample bottom fish at sites on the continental shelf previously sampled with a small beam trawl, but using a large atter trawl to check the validity of the beam trawl's samples; to investigate the biology of the more important species. 177 midwater trawls were successfully completed at 59 stations.

A Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:

AADC-00038 AADC-00068 AADC-00073 AADC-00075 AADC-00080 AADC-00082 c88_data

The fields in this dataset are:

Cruises Date Location Latitude Longitude Species Gear Length Weight Sex Gonad Eye Otolith Stomach Lifestage Family

The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987.

WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields:

AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection

ACPD comes as an Arc/Info EXPORT file originally called "ACPD.E00" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command.

References to the WCPD data set can be found in:

• SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.
• D. Rhind. "Cartographically-related research at Birkbeck College 1987-91" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.

The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK.

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.

Abstract of paperRapid human-driven environmental changes are impacting animal populations around the world. Currently, land-use and climate change are two of the biggest pressures facing biodiversity. However, studies investigating the impacts of these pressures on population trends often do not consider potential interactions between climate and land-use change. Further, a population’s climatic position (how close the ambient temperature and precipitation conditions are to the species’ climatic tolerance limits) is known to influence responses to climate change but has yet to be investigated with regard to its influence on land-use change responses over time. Consequently, important variation across species’ ranges in responses to environmental changes may be being overlooked. Here, we combine data from the Living Planet and BioTIME databases to carry out a global analysis exploring the impacts of land use, habitat loss, climatic position, climate change, and the interactions between these variables, on vertebrate population trends. By bringing these datasets together, we analyse over 7,000 populations across 42 countries. We find that land-use change is interacting with climate change and a population’s climatic position to influence rates of population change. Moreover, features of a population’s local landscape (such as surrounding land cover) play important roles in these interactions. For example, populations in agricultural land uses where maximum temperatures were closer to their hot thermal limit, declined at faster rates when there had also been rapid losses in surrounding semi-natural habitat. The complex interactions between these variables on populations highlights the importance of taking intraspecific variation and interactions between local and global pressures into account. Understanding how drivers of change are interacting and impacting populations, and how this varies spatially, is critical if we are to identify populations at risk, predict species’ responses to future environmental changes and produce suitable conservation strategies.Information on data and code 'Code_to_run_models_JJW_GCB.R' contains the R script to run all the candidate models and then compare them.'Data_JJW_GCB.rds' contains the data used to run the candidate models that investigated how rate of population change was affected by land-use type and change, a population’s climatic position, and the rate of climate change experienced.MetadataBinomial = Species name (HIDDEN if classed as confidential within the Living Planet Index database)Class = The vertebrate Classloc_id = ID based on the population's location (latitude and longitude)data = The database the population’s time-series data was acquired from, either the Living Planet Index (LPI) or BioTIME databaseBaselineLU2 = Starting land-use typeTmax_rate, Tmin_rate, Ppmax_rate, Ppmin_rate = The average annual rate of change in maximum temperature of the warmest month, minimum temperature of the coldest month, precipitation of the wettest month and precipitation of the driest month, respectively, over the length of the population time-seriesPercentPVV2_rate = The average annual rate of change in the percentage of semi-natural habitat within a 1-km radius of the population, over the length of the population time-seriesstand_dist = The standardised distance of a population from their species’ geographic range edgeStart_Tmax_Pos, Start_Tmin_Pos, Start_Ppmax_Pos, Start_Ppmin_Pos = The maximum temperature of the warmest month (Tmax), minimum temperature of the coldest month (Tmin), precipitation of the wettest month (Ppmax), and precipitation of the driest month (Ppmin), a population experienced in the first year they were measured, relative to the species-level upper and lower realised thermal (for Tmax and Tmin) or precipitation (for Ppmax and Ppmin) tolerance limitslambda_mean = The average logged annual rate of population change

Metadata record for data from ASAC Project 1180 Global change, biodiversity and conservation in terrestrial and coastal ecosystems on Heard and McDonald Islands: statistical models for monitoring and predicting effects of climate change and local human impacts on invertebrates.

We identified the major environmental variables as altitude and vegetation type. We selected sites for sampling by designing a stratified survey that sampled along gradients of altitude and vegetation type. We included 60 sites in the survey design, distributed across Heard Island in five areas or blocks: Round Hill, Scarlet Hill, Cape Lockyer, Long Beach and Mt Drygowski. This level of sampling was achievable within the five-month period and was sufficient to produce reliable statistical models of invertebrate distribution in the subantarctic (Davies and Melbourne, 1999).

We included altitude as a continuous variable (i.e. altitude was measured to +/- 5 meters). For the purpose of getting a reasonably balanced design and for selecting sites we defined altitude classes as: 0-100 m, 100-200 m, 200 - 300 m, 300 - 400 m, 400-500 m, 500-600 m, 600 m +. We included five vegetation categories, which were not meant to cover all kinds of vegetation but instead span the range of vegetation types. Our vegetation classes were Poa cookii dominated (greater than 75%), Pringlea dominated (greater than 75%), Azorella dominated (greater than 75%), feldmark (less than 50% vegetation but with the vegetation that is present consisting of at least 50% moss), and 'patchy Azorella' (50-75% Azorella but less than 75% total vegetation). Not all vegetation classes occurred at all altitudes so we have crossed vegetation with altitude to give roughly 13-16 altitude-vegetation combinations. At most we managed to find between 10-13 sites in each of five areas giving us a total of 60 sites.

At all of our sites, we took pitfall samples, and at a selected subset of sites (20 sites) we did hand searches to get density estimates. The trapping took place between January and March 2001. The species that we trapped were: Anatalanta aptera, Myro kerguelenensis, Canonopsis sericeus, Ectemnorhinus viridis, Bothrometopus brevis, Bothrometopus gracilipes, Notodiscus hookeri, Embryonopsis halticella, Calycopteryx mosleyi, Amalopteryx maritima. We produced statistical models that describe the distribution of these species over Heard Island, based on altitude and vegetation type.

We took standardised photographs of a 10 m transect at each of our 60 sample sites. These will allow us to look for changes in vegetation composition at these sites in the future. The photographs are in a digital format.

The fields in this dataset are:

Region Altitude Site Code Slope Aspect Latitude Longitude Dirt Species Date Time Vegetation Type Sample Number

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

High-latitude ecosystems are experiencing the most rapid warming on earth, expected to trigger a diverse array of ecological responses. Climate warming affects the ecophysiology of fish, and fish close to the cold end of their thermal distribution are expected to increase somatic growth from increased temperatures and a prolonged growth season, which in turn affects maturation schedules, reproduction and survival, boosting population growth. Accordingly, fish species living in ecosystems close to their northern range edge should increase in relative abundance and importance, and possibly displace cold-water adapted species. We aim to document if and how population-level effects of warming are mediated by individual-level responses to increased temperatures, shifts in community structure and composition in high-latitude ecosystems. We studied 11 cool-water adapted freshwater fish populations in communities dominated by cold-water-adapted species to investigate changes in the relative i...

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for preprint titled "COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe" https://doi.org/10.1101/2021.01.27.21250658

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

===========

Dataset file: 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv

Dataset graphical preview: 2.0.0.INFOGRAPHIC_CloudFraction_vs_COVID-19_mortality_Europe_March-December_2020.png

DATASET: 444 rows (records), with the following fields:

"Country" : Country name; 37 European countries included.

"Date": Date stamp at the collection time. Data collection was performed in the last day of every month. Date format: YYYY-MM-DD

"Month_Key" : Date stamp at the collection time, formatted for easier monthly time series analysis. Date format: YYYY-MM

"Month_Fct2020" Date stamp at the collection time,formatted for easier graphing, as a string with names of the months (in English).

"Deaths_per_1Mpop" : Monthly mortality from COVID-19 raported in the country, reported as number of COVID-19 deaths per 1 million population of the country, in that particular month / country. NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" : Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" : Insolation average (solar irradiance at ground level), in that particular month / country. It is expressed in Watt / square meter of the ground surface. Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, CERES / FLASHFlux team, 2020, https://neo.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M )

"Cloud_Fraction" : Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover), as decimal fraction of the sky obscured by clouds, in that particular month / country. Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory, MODIS Atmosphere Science Team, 2020, https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR )

"CENTR_latitude" and "CENTR_longitude" : Latitude and Longitude of the country centroid, for each country. Data derived from Google LLC, "Dataset publishing language: country centroids", https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously); it is redundantly included for easier monthly sectional analysis of the data.

===========

Versioning of the dataset: MAJOR: changes yearly; 1 = 2020 MINOR: changes if new monthly data is added in that particular year. PATCH: Changes only if errors or minor edits were performed.

===========

CHANGELOG:

Version 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv - CERES/FLASHFLUX data for August-December 2020 became available at new links at nasa.gov - These data were gathered, analyzed and introduced in this dataset (2.0.0). - updated links for CERES/FLASHFLUX and MODIS dataset - added DOI link for preprint - minor edits on text. -Dataset file source for this version (internal analysis source file): db_covid_all-ANALYSIS.2020-all-year_versiunea18d.csv

Version 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv First version Dataset file source for this version (internal analysis source file): db_covid_all-ANALYSIS.2020-09-22_r10.csv

These are data described in the paper "The brittle boulders of dwarf planet Ceres" by Schröder et al., submitted to the Planetary Science Journal. The data concern boulder counts for craters on the dwarf planet Ceres.

Boulder counts for all 58 craters in Table 1 of the paper are provided as text files with 3 columns: (1) longitude and (2) latitude, both in degrees, and (3) boulder diameter in meters. Generally, boulder identifications are only reliable above 3 image pixels (105 m), but the data files contain all boulders that we (tentatively) identified, including those smaller than 105 m.

We also provide figures in PDF format, one for each crater, of the spatial boulder distribution. The figures have the format of Fig. 12 in the paper: Green, small dots represent boulders with a size between 3 and 4 pixels (105 m < d < 140 m). Red, large dots represent boulders larger than 4 pixels (d > 140 m). Boulders smaller than 105 m are not shown.

The coordinates in degrees of latitude and longitude of all craters in Table 1 are provided as a text file.

The relationship between colony area and population density of Adelie Penguins Pygoscelis adeliae was examined to determine whether colony area, measured from aerial or satellite imagery, could be used to estimate population density, and hence detect changes in populations over time. Using maps drawn from vertical aerial photographs of Adelie Penguin colonies in the Mawson region, pair density ranged between 0.1 and 3.1 pairs/m2, with a mean of 0.63 - 0.3 pairs/m2. Colony area explained 96.4% of the variance in colony populations (range 90.4 - 99.6%) for 979 colonies at Mawson. Mean densities were not significantly different among the 19 islands in the region, but significant differences in mean pair density were observed among colonies in Mawson, Whitney Point (Casey, East Antarctica) and Cape Crozier (Ross Sea) populations.

This work was completed as part of ASAC project 1219 (ASAC_1219).

The fields in this dataset are:

Island Latitude Longitude Date Colony area Breeding Pairs Breeding Pairs per square metre Area per nest Number of nests Number of adults

The data associated with this record is accessible via the parent record - https://data.aad.gov.au/metadata/Historical_Fish_data

This dataset contains the data from Voyage 7.2 (HIMS) 1989-90 of the Aurora Australis. There were three objectives of the fish program: To assess the distribution and abundance of demersal fish on the shelf in the AFZ around Heard Island; to investigate the biology of the more important species; to take samples to determine by mitochondrial DNA (mtDNA) analysis whether the Heard Island Champsocephalus gunnari are a genetically distinct stock from those around Kerguelen Islands. The observations were taken from around Heard Island between May and June 1990. The data contains temperature and salinity data from a CTD survey and the results from various trawl surveys. The major species were Dissostichus eleginoides, macrourus holotrachys, Champsocephalus gunnari and Notothenia squamifrons. Numbers, species identity, guts and gonad data were obtained. This is a subset of the data for the whole voyage.

A Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:

AADC-00038 AADC-00068 AADC-00073 AADC-00080 AADC-00082 c88_data

The fields in this dataset are:

Cruises Date Location Latitude Longitude Species Gear Length Weight Sex Gonad Eye Otolith Stomach Lifestage Family

In this study, we sampled 54 Scots pine populations from the Norwegian coast over the Arctic Circle to western Russia covering 47.3 longitudes or more than 1/8th of the earth’s circumference, which represents the most comprehensive coverage of Northern Europe to date. We inferred variation in autumn phenology and dormancy progression from freeze hardiness tests conducted on >5000 seedlings, of which >900 seedlings from 24 populations were genotyped using genotyping-by-sequencing (GBS). Our main goal was to evaluate adaptive responses in Scots pine at phenotype and genotype levels. Evaluation of cold hardiness along environmental and geographical gradients would contribute to an understanding of the performance of these gradients for predicting freeze damage levels. The genotype data allow evaluation of genetic variance across landscapes and thus shed light on the degree of genetic-environmental association and the recolonization history of Scots pine in Scandinavia.

Environmental variables for each population was extracted based on their latitude and longitude at origin from 68 different high resolution environmental grids. These variables were then used for phenotype- and genotype-environment association analyses. A description of all variables can be found in Table S2 in “Document S1. Supplemental methods, Supplemental Figures 1–4, and Supplemental Tables 1–6.”, available from doi.org/10.1016/j.xplc.2020.100139

The LandScan data set is a worldwide population database compiled on a 30" X 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other data sets. LandScan has been developed as part of the Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk. The LandScan files are available via the internet in ESRI grid format by continent and for the world. You can access the data files after user registration through the data links. For an overview of the methods used to develop LandScan, please read the documentation and FAQs.

[Summary provided by Oak Ridge National Laboratory]

---- Public Summary from Project ---- Leopard seals are usually seen in the pack-ice where they pup on the ice and where they must first face life at sea. However at Macquarie Island, well to the north of the ice, for 50 years now there has been the odd phenomenon of 'Leopard seal years'. At seemingly semi-regular periods (~3-4 years) considerable numbers (can be greater than 100) of leopard seals arrive at the island; and then virtually none are seen for some more years. The periodicity of these arrivals has been striking.

Thus it seems that young leopard seals (which is the group arriving in poor condition on Macquarie Island) suffer acute food shortages in the pack-ice zone every 3-4 years. This project will continue to record these events and tag and weigh the seals which come ashore. This will allow the long-term dataset to continue and give some more information about the seals which arrive. It is also planned to glue some satellite recorders to the seals so that their journeys after M.I. can be known.

Data are collected when seals are seen on beach. Since the 1980s few seals have been seen so data are sparse but significant.

Currently the dataset contains the number of leopard seals sighted at Macquarie Island each year and a record of sightings of Leopard Seals from 1948 till 2002 (some years are omitted due to unavailability of data, see quality information). Details on the sightings include date and location of sighting and condition of the seal.

The fields in the dataset for the number of seals sighted each year at Macquarie Island are:

Year Number of seals.

The fields in the dataset detailing the sightings of Leopard Seals on Macquarie Island from 1948 till 2002 include the following:

Seal ID: Each seal has been allocated a unique ID number. This acts as a means of tracking the seal if a tag is replaced or removed.

Tag #1 and Tag #2: Tag numbers include plastic tags attached to the seals flippers and substitute tag numbers allocated to those seals marked with paint in 1959 and those seals resighted by length and/or a distinguishing feature or injury.

Information on plastic tags:

-All tags used from 1976-1981 were yellow plastic - except 50 (30/9/76) which is red plastic diamond shaped, and 90a which is metal.

-Tag numbers followed by a in 1976 are coffin shaped (note: a prefix of 0 was used in original tag rather than an a following the number).

-Tag numbers followed by a in 1977 are combinations of shovel and coffin shaped parts (note: a prefix of 0 was used in original tag rather than an a following the number).

-Tag numbers not followed by a in 1977 are shovel-shaped.

-Tags used by 1986 were the 'Jumbo Rototag' which are smaller and made of less flexible plastic than the 'Allflex' tags originally used.

-See references below for further information on tags and methods of tagging used.

Information on substitute or'S' tags

-Tags prefixed with S are substitute tags. Seals with a tag prefixed by S were not physically tagged with a plastic or metal tag. This 'tag number' was allocated when collating data from years when plastic tagging were not used and resights of seals were determined by either coloured markings painted on the seals (as in 1959) or by a combination of length, sex, distinguishing features or injuries.

-S Tag numbers were allocated in date order of the original or 'New' sighting. Hence 'tag' S1 was allocated to the first seal sighted and then resighted in 1949.

-Note: There are some instances where the original recorder of the sightings did not note any distinguishing features or paint markings on the seal but later recorded that the seal had been resighted. When this occurred the 'word' of the recorder was taken and an S tag allocated.

Date: Date of sighting whether initial sighting or a resighting of the same seal.

Location Codes: This field notes the location code for the area on Macquarie Island where the seal was sighted. The code corresponds to a grid reference on Macquarie Island that was originally used for locating Elephant Seal sightings.

A listing of these reference codes is also attached to this dataset. The fields in the location code dataset are: Location Name, Location ID, Latitude and Longitude.

Within the original records a number of locations were noted using outdated or informal names. These locations were renamed with the reference code now used for that location. A listing of the informal names and the location codes they respond to has been included in the Location Codes worksheet for reference.

Sex: the sex of the seal is noted in this column as either: M = Male or F = Female.

Length: The nose to tail length of the seal is noted in centimetres.

Condition: This field details the general condition of the Leopard Seal. The coding is as follows: G = Good, F = Fair, P = Poor, T = Thin, E = Emancipated, D = Dead and K = Killed.

Comments on Condition: This field is used to note any additional details regarding the conditio...

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Lake. The data include parameters of insect|paleolimnology with a geographic location of Greenland. The time period coverage is from 10708 to -62 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Shanks et al. Ecol MonoData are a combination of field-collected data on barnacle populations at stations from San Diego, California to La Push, Washington. Stations names are in column A and they were cataloged (column B) as either rocky shores (1, continuous rocks intertidal for at least 50 m) or rocks within beaches (2, rocky shore <50 m). The latitude and longitude (columns D and E) are precise locations using the ‘place’ tool in Google Earth. Columns F and G are alongshore wind stress (a proxy for upwelling/downwelling) and average wave height calculated for each station latitude. See Shanks et al (in press) for descriptions of the methods. Daily solar radiation (column H) is from the National Solar Radiation Data Base (http://rredc.nrel.gov/solar/old_data/nsrdb/). Average surfzone width (Column I) was determined from Google Earth images (See Shanks et al, in press for methods). Intertidal width and beach angle (columns J and K) were measured in the field at the time the station...

The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992.

    Four cruises took place: February 3 - March 9, March 19 - April 15,
    August 5 - September 18, and September 24 - October 21. A fifth benthic
    cruise and sediment trap legs were added. During the first cruise
    (TT007), 15 stations were occupied along 140 deg W longitude from
    12 deg N latitude to 12 deg S latitude. During the second cruise
    (TT008), data were collected at 8 stations along 140 deg W longitude
    from 9 deg S latitude to 9 deg N latitude. During the third cruise
    (TT011), data were collected at 15 stations along 140 deg W from 12 deg N
    latitude to 12 deg S latitude. During the fourth cruise (TT012), data were
    collected at 5 stations along 140 deg W longitude from 17 deg S
    latitude to the equator.

    Population abundances of bacteria and phytoplankton were measured
    on the first and third cruises. Predawn water samples were taken at
    each station using the CTD rosette water sampler. The samples were
    preserved by freezing them in liquid nitrogen. Abundances of
    bacteria, cyanobacteria (Synechococcus and Prochlorococcus), and
    chlorophyll-containing nanoplankton were determined from the preserved
    samples by flow cytometry (FCM) with internal standards of fluorescent
    beads.

    The data is public domain and can be retrieved on-line at
    "http://usjgofs.whoi.edu/jg/dir/jgofs/"

    [The information in this summary was derived from the JGOFS
    World Wide Web pages.]

This dataset contains the data from Voyage 7.2 (HIMS) 1989-90 of the Aurora Australis. There were three objectives of the fish program: To assess the distribution and abundance of demersal fish on the shelf in the AFZ around Heard Island; to investigate the biology of the more important species; to take samples to determine by mitochondrial DNA (mtDNA) analysis whether the Heard Island Champsocephalus gunnari are a genetically distinct stock from those around Kerguelen Islands. The observations were taken from around Heard Island between May and June 1990. The data contains temperature and salinity data from a CTD survey and the results from various trawl surveys. The major species were Dissostichus eleginoides, macrourus holotrachys, Champsocephalus gunnari and Notothenia squamifrons. Numbers, species identity, guts and gonad data were obtained. This is a subset of the data for the whole voyage.

A Microsoft Access database containing data from this cruise, plus several others is available for download from the provided URL. The Entry ID's of the other metadata records also related to this data are:

AADC-00038 AADC-00068 AADC-00073 AADC-00080 AADC-00082 c88_data

The fields in this dataset are:

Cruises Date Location Latitude Longitude Species Gear Length Weight Sex Gonad Eye Otolith Stomach Lifestage Family