Introduction to pangaear

pangaear is a data retrieval interface for the World Data Center PANGAEA (https://www.pangaea.de/). PANGAEA archieves published Earth & Environmental Science data under the following subjects: agriculture, atmosphere, biological classification, biosphere, chemistry, cryosphere, ecology, fisheries, geophysics, human dimensions, lakes & rives, land surface, lithosphere, oceans, and paleontology.

Installation

If you’ve not installed it yet, install from CRAN:

install.packages("pangaear")

Or the development version:

devtools::install_github("ropensci/pangaear")

Load pangaear

library("pangaear")

Search for data

pg_search is a thin wrapper around the GUI search interface on the page https://www.pangaea.de/. Everything you can do there, you can do here.

For example, query for the term ‘water’, with a bounding box, and return only three results.

pg_search(query = 'water', bbox = c(-124.2, 41.8, -116.8, 46.1), count = 3)
#> # A tibble: 3 x 6
#>   score doi            size size_measure citation                                                 supplement_to                                                                      
#>   <dbl> <chr>         <dbl> <chr>        <chr>                                                    <chr>                                                                              
#> 1 13.0  10.1594/PANG…     4 datasets     Krylova, EM; Sahling, H; Janssen, R (2010): A new genus… Krylova, EM; Sahling, H; Janssen, R (2010): Abyssogena: a new genus of the family …
#> 2 12.8  10.1594/PANG…     2 datasets     Simonyan, AV; Dultz, S; Behrens, H (2012): Diffusion tr… Simonyan, AV; Dultz, S; Behrens, H (2012): Diffusive transport of water in porous …
#> 3  8.78 10.1594/PANG…  1148 data points  WOCE Surface Velocity Program, SVP (2006): Water temper… <NA>

The resulting data.frame has details about different studies, and you can use the DOIs (Digital Object Identifiers) to get data and metadata for any studies you’re interested in.

Another search option

There’s another search option with the pg_search_es function. It is an interface to the Pangaea Elasticsearch interface. This provides a very flexible interface for search Pangaea data - though it is different from what you’re used to with the Pangaea website.

(res <- pg_search_es())
#> # A tibble: 10 x 46
#>    `_index` `_type` `_id` `_score` `_source.intern… `_source.parent… `_source.minEle… `_source.sf-aut… `_source.parent… `_source.techKe… `_source.geocod… `_source.sp-log…
#>  * <chr>    <chr>   <chr>    <dbl> <chr>            <chr>                       <dbl> <chr>                       <int> <list>           <list>                      <int>
#>  1 pangaea… panmd   9018…        1 2020-01-17T15:1… https://doi.org…             -0.4 Anhaus Philipp#…           901247 <chr [654]>      <chr [5]>                       1
#>  2 pangaea… panmd   9017…        1 2020-01-17T15:1… https://doi.org…              2   Anhaus Philipp#…           901247 <chr [652]>      <chr [5]>                       1
#>  3 pangaea… panmd   9017…        1 2020-01-17T15:1… https://doi.org…              2   Anhaus Philipp#…           901247 <chr [652]>      <chr [5]>                       1
#>  4 pangaea… panmd   9017…        1 2020-01-17T15:1… https://doi.org…              2   Anhaus Philipp#…           901247 <chr [652]>      <chr [5]>                       1
#>  5 pangaea… panmd   9017…        1 2020-01-17T15:1… <NA>                          2   Vuilleumier Lau…               NA <chr [51]>       <chr [6]>                       3
#>  6 pangaea… panmd   9017…        1 2020-01-17T15:1… https://doi.pan…              0.3 Peeken Ilka#Mur…           901742 <chr [101]>      <chr [6]>                       3
#>  7 pangaea… panmd   9017…        1 2020-01-17T15:1… <NA>                         NA   Schreuder Laura…               NA <chr [37]>       <chr [3]>                       1
#>  8 pangaea… panmd   9017…        1 2020-01-17T15:1… https://doi.org…              0   Schreuder Laura…           901739 <chr [39]>       <chr [7]>                       1
#>  9 pangaea… panmd   9016…        1 2020-01-17T15:1… <NA>                         10   Augustine John$…               NA <chr [25]>       <chr [6]>                       3
#> 10 pangaea… panmd   9016…        1 2020-01-17T15:1… <NA>                          2   Augustine John$…               NA <chr [29]>       <chr [6]>                       3
#> # … with 34 more variables: `_source.agg-campaign` <list>, `_source.agg-author` <list>, `_source.eastBoundLongitude` <dbl>, `_source.URI` <chr>, `_source.agg-pubYear` <int>,
#> #   `_source.minDateTime` <chr>, `_source.agg-geometry` <chr>, `_source.xml-thumb` <chr>, `_source.xml` <chr>, `_source.sf-idDataSet` <int>, `_source.elevationGeocode` <chr>,
#> #   `_source.agg-method` <list>, `_source.maxDateTime` <chr>, `_source.xml-sitemap` <chr>, `_source.westBoundLongitude` <dbl>, `_source.northBoundLatitude` <dbl>,
#> #   `_source.sp-dataStatus` <int>, `_source.nDataPoints` <int>, `_source.sp-hidden` <lgl>, `_source.agg-location` <list>, `_source.internal-source` <chr>,
#> #   `_source.agg-basis` <chr>, `_source.southBoundLatitude` <dbl>, `_source.boost` <dbl>, `_source.oaiSet` <list>, `_source.maxElevation` <dbl>, `_source.agg-mainTopic` <list>,
#> #   `_source.agg-topic` <list>, `_source.agg-project` <list>, `_source.meanPosition.lat` <dbl>, `_source.meanPosition.lon` <dbl>, `_source.geoCoverage.type` <chr>,
#> #   `_source.geoCoverage.coordinates` <list>, `_source.geoCoverage.geometries` <list>

The returned data.frame has a lot of columns. You can limit columns returned with the source parameter.

There are attributes on the data.frame that give you the total number of results found as well as the max score found.

attributes(res)
#> $names
#>  [1] "_index"                          "_type"                           "_id"                             "_score"                          "_source.internal-datestamp"     
#>  [6] "_source.parentURI"               "_source.minElevation"            "_source.sf-authortitle"          "_source.parentIdDataSet"         "_source.techKeyword"            
#> [11] "_source.geocodes"                "_source.sp-loginOption"          "_source.agg-campaign"            "_source.agg-author"              "_source.eastBoundLongitude"     
#> [16] "_source.URI"                     "_source.agg-pubYear"             "_source.minDateTime"             "_source.agg-geometry"            "_source.xml-thumb"              
#> [21] "_source.xml"                     "_source.sf-idDataSet"            "_source.elevationGeocode"        "_source.agg-method"              "_source.maxDateTime"            
#> [26] "_source.xml-sitemap"             "_source.westBoundLongitude"      "_source.northBoundLatitude"      "_source.sp-dataStatus"           "_source.nDataPoints"            
#> [31] "_source.sp-hidden"               "_source.agg-location"            "_source.internal-source"         "_source.agg-basis"               "_source.southBoundLatitude"     
#> [36] "_source.boost"                   "_source.oaiSet"                  "_source.maxElevation"            "_source.agg-mainTopic"           "_source.agg-topic"              
#> [41] "_source.agg-project"             "_source.meanPosition.lat"        "_source.meanPosition.lon"        "_source.geoCoverage.type"        "_source.geoCoverage.coordinates"
#> [46] "_source.geoCoverage.geometries" 
#> 
#> $row.names
#>  [1]  1  2  3  4  5  6  7  8  9 10
#> 
#> $class
#> [1] "tbl_df"     "tbl"        "data.frame"
#> 
#> $total
#> [1] 390620
#> 
#> $max_score
#> [1] 1
attr(res, "total")
#> [1] 390620
attr(res, "max_score")
#> [1] 1

To get to the DOIs for each study, use

gsub("https://doi.org/", "", res$`_source.URI`)
#>  [1] "10.1594/PANGAEA.901810"                        "10.1594/PANGAEA.901736"                        "10.1594/PANGAEA.901733"                       
#>  [4] "10.1594/PANGAEA.901732"                        "https://doi.pangaea.de/10.1594/PANGAEA.901710" "https://doi.pangaea.de/10.1594/PANGAEA.901709"
#>  [7] "10.1594/PANGAEA.901739"                        "10.1594/PANGAEA.901738"                        "https://doi.pangaea.de/10.1594/PANGAEA.901697"
#> [10] "https://doi.pangaea.de/10.1594/PANGAEA.901695"

Get data

The function pg_data fetches datasets for studies by their DOIs.

res <- pg_data(doi = '10.1594/PANGAEA.807580')
res[[1]]
#> <Pangaea data> 10.1594/PANGAEA.807580
#>   parent doi: 10.1594/PANGAEA.807580
#>   url:        https://doi.org/10.1594/PANGAEA.807580
#>   citation:   Schiebel, Ralf; Waniek, Joanna J; Bork, Matthias; Hemleben, Christoph (2001): Physical oceanography during METEOR cruise M36/6. PANGAEA, https://doi.org/10.1594/PANGAEA.807580, In supplement to: Schiebel, R et al. (2001): Planktic foraminiferal production stimulated by chlorophyll redistribution and entrainment of nutrients. Deep Sea Research Part I: Oceanographic Research Papers, 48(3), 721-740, https://doi.org/10.1016/S0967-0637(00)00065-0
#>   path:       /Users/sckott/Library/Caches/R/pangaear/10_1594_PANGAEA_807580.txt
#>   data:
#> # A tibble: 32,179 x 13
#>    Event       `Date/Time`    Latitude Longitude `Elevation [m]` `Depth water [m… `Press [dbar]` `Temp [°C]`   Sal `Tpot [°C]` `Sigma-theta [kg/m… `Sigma in situ [kg… `Cond [mS/cm]`
#>    <chr>       <chr>             <dbl>     <dbl>           <int>            <dbl>          <int>       <dbl> <dbl>       <dbl>               <dbl>               <dbl>          <dbl>
#>  1 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             0                 0        15.7  35.7        15.7                26.4                26.4           44.4
#>  2 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             0.99              1        15.7  35.7        15.7                26.4                26.4           44.4
#>  3 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             1.98              2        15.7  35.7        15.7                26.4                26.4           44.4
#>  4 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             2.97              3        15.7  35.7        15.7                26.4                26.4           44.4
#>  5 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             3.96              4        15.7  35.7        15.7                26.4                26.4           44.4
#>  6 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             4.96              5        15.7  35.7        15.7                26.4                26.4           44.4
#>  7 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             5.95              6        15.7  35.7        15.7                26.4                26.4           44.4
#>  8 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             6.94              7        15.7  35.7        15.7                26.4                26.4           44.4
#>  9 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             7.93              8        15.7  35.7        15.7                26.4                26.4           44.4
#> 10 M36/6-CTD-… 1996-10-14T12…     49.0     -16.5           -4802             8.92              9        15.7  35.7        15.7                26.4                26.4           44.4
#> # … with 32,169 more rows

Search for data then pass one or more DOIs to the pg_data function.

res <- pg_search(query = 'water', bbox = c(-124.2, 41.8, -116.8, 46.1), count = 3)
pg_data(res$doi[3])[1:3]
#> [[1]]
#> <Pangaea data> 10.1594/PANGAEA.405695
#>   parent doi: 10.1594/PANGAEA.405695
#>   url:        https://doi.org/10.1594/PANGAEA.405695
#>   citation:   WOCE Surface Velocity Program, SVP (2006): Water temperature and current velocity from surface drifter SVP_9524470. PANGAEA, https://doi.org/10.1594/PANGAEA.405695
#>   path:       /Users/sckott/Library/Caches/R/pangaear/10_1594_PANGAEA_405695.txt
#>   data:
#> # A tibble: 192 x 10
#>    `Date/Time`      Latitude Longitude `Depth water [m]` `Temp [°C]` `UC [cm/s]` `VC [cm/s]` `Lat e` `Lon e`  Code
#>    <chr>               <dbl>     <dbl>             <int>       <dbl>       <dbl>       <dbl>   <dbl>   <dbl> <int>
#>  1 1995-12-21T18:00     42.9     -125.                 0        12.4       NA          NA     0.0001  0.0001     1
#>  2 1995-12-22T00:00     42.9     -125.                 0        12.4        4.24      -35.2   0       0          1
#>  3 1995-12-22T06:00     42.8     -125.                 0        12.4       -7.8       -38.7   0.0002  0.0001     1
#>  4 1995-12-22T12:00     42.7     -125.                 0        12.4      -12.7       -23.1   0.0001  0.0001     1
#>  5 1995-12-22T18:00     42.7     -125.                 0        12.4      -15.1       -13.7   0       0          1
#>  6 1995-12-23T00:00     42.7     -125.                 0        12.3      -24.1        -9.15  0.0001  0.0001     1
#>  7 1995-12-23T06:00     42.7     -125.                 0        12.3      -38.4         0.65  0.0001  0.0001     1
#>  8 1995-12-23T12:00     42.7     -125.                 0        12.3      -37.2        15.8   0       0          1
#>  9 1995-12-23T18:00     42.7     -125.                 0        12.3      -25.4        29.6   0.0001  0.0001     1
#> 10 1995-12-24T00:00     42.8     -125.                 0        12.4      -18.5        35.1   0.0002  0.0002     1
#> # … with 182 more rows
#> 
#> [[2]]
#> NULL
#> 
#> [[3]]
#> NULL

OAI-PMH metadata

OAI-PMH is a standard protocol for serving metadata around objects, in this case datasets. If you are already familiar with OAI-PMH you are in luck as you can can use what you know here. If not familiar, it’s relatively straight-forward.

Note that you can’t get data through these functions, rather only metadata about datasets.

Identify the service

pg_identify()
#> <Pangaea>
#>   repositoryName: PANGAEA - Data Publisher for Earth & Environmental Science
#>   baseURL: https://ws.pangaea.de/oai/provider
#>   protocolVersion: 2.0
#>   adminEmail: [email protected]
#>   adminEmail: [email protected]
#>   earliestDatestamp: 2015-01-01T00:00:00Z
#>   deletedRecord: transient
#>   granularity: YYYY-MM-DDThh:mm:ssZ
#>   compression: gzip
#>   description: oaipangaea.de:oai:pangaea.de:doi:10.1594/PANGAEA.999999

List metadata formats

pg_list_metadata_formats()
#>   metadataPrefix                                                  schema                           metadataNamespace
#> 1         oai_dc          http://www.openarchives.org/OAI/2.0/oai_dc.xsd http://www.openarchives.org/OAI/2.0/oai_dc/
#> 2         pan_md       http://ws.pangaea.de/schemas/pangaea/MetaData.xsd              http://www.pangaea.de/MetaData
#> 3            dif  http://gcmd.gsfc.nasa.gov/Aboutus/xml/dif/dif_v9.4.xsd  http://gcmd.gsfc.nasa.gov/Aboutus/xml/dif/
#> 4       iso19139                http://www.isotc211.org/2005/gmd/gmd.xsd            http://www.isotc211.org/2005/gmd
#> 5  iso19139.iodp                http://www.isotc211.org/2005/gmd/gmd.xsd            http://www.isotc211.org/2005/gmd
#> 6      datacite3   http://schema.datacite.org/meta/kernel-3/metadata.xsd         http://datacite.org/schema/kernel-3
#> 7      datacite4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd         http://datacite.org/schema/kernel-4

List identifiers

pg_list_identifiers(from = Sys.Date() - 2, until = Sys.Date())

List sets

pg_list_sets()
#> # A tibble: 282 x 2
#>    setSpec   setName                                        
#>    <chr>     <chr>                                          
#>  1 ACD       PANGAEA set / keyword 'ACD' (2 data sets)      
#>  2 ASPS      PANGAEA set / keyword 'ASPS' (59 data sets)    
#>  3 AWIXRFraw PANGAEA set / keyword 'AWIXRFraw' (1 data sets)
#>  4 BAH1960   PANGAEA set / keyword 'BAH1960' (2 data sets)  
#>  5 BAH1961   PANGAEA set / keyword 'BAH1961' (2 data sets)  
#>  6 BAH1962   PANGAEA set / keyword 'BAH1962' (7 data sets)  
#>  7 BAH1963   PANGAEA set / keyword 'BAH1963' (7 data sets)  
#>  8 BAH1964   PANGAEA set / keyword 'BAH1964' (7 data sets)  
#>  9 BAH1965   PANGAEA set / keyword 'BAH1965' (7 data sets)  
#> 10 BAH1966   PANGAEA set / keyword 'BAH1966' (6 data sets)  
#> # … with 272 more rows

List records

pg_list_records(from = Sys.Date() - 1, until = Sys.Date())

Get a record

pg_get_record(identifier = "oai:pangaea.de:doi:10.1594/PANGAEA.788382")
#> $`oai:pangaea.de:doi:10.1594/PANGAEA.788382`
#> $`oai:pangaea.de:doi:10.1594/PANGAEA.788382`$header
#> # A tibble: 1 x 3
#>   identifier                                datestamp            setSpec                                           
#>   <chr>                                     <chr>                <chr>                                             
#> 1 oai:pangaea.de:doi:10.1594/PANGAEA.788382 2020-01-18T03:11:42Z citable;supplement;topicChemistry;topicLithosphere
#> 
#> $`oai:pangaea.de:doi:10.1594/PANGAEA.788382`$metadata
#> # A tibble: 1 x 13
#>   title            creator     source                publisher date   type   format  identifier       description               language rights       coverage               subject 
#>   <chr>            <chr>       <chr>                 <chr>     <chr>  <chr>  <chr>   <chr>            <chr>                     <chr>    <chr>        <chr>                  <chr>   
#> 1 Trace metals in… Demina, Ly… P.P. Shirshov Instit… PANGAEA   2012-… Datas… applic… https://doi.pan… Bioaccumulation of trace… en       CC-BY-3.0: … MEDIAN LATITUDE: 29.1… Archive…