FREYA WP2 User Story 1	As a data center, I want to see the citations of publications that use my repository for the underlying data, so that I can demonstrate the impact of our repository.

It is important for repositories of scientific data to monitor and report on the impact of the data they store. One useful proxy of that impact are citations of publications accompanying the deposited data.

This notebook uses the DataCite GraphQL API to retrieve data (a.k.a. works) and their citations from three different repositories: PANGAEA, DRYAD and Global Biodiversity Information Facility, using polarstern, butterfly and Lake Malawi as example queries respectively.

Goal: By the end of this notebook you should be able to:

• Retrieve works for a chosen repository and query, along with associated metrics such as citation, view and download counts;
• Visualise the work counts over time, e.g.
  
• Present the works in a tabular format and download them in a single BibTeX file;
• For a given work, retrieve all the citations, present them in a tabular format and then download them in a single BibTeX file.

Install libraries and prepare GraphQL client

%%capture
# Install required Python packages
!pip install gql requests numpy pandas

# Prepare the GraphQL client
import requests
from IPython.display import display, Markdown
from gql import gql, Client
from gql.transport.requests import RequestsHTTPTransport

_transport = RequestsHTTPTransport(
    url='https://api.datacite.org/graphql',
    use_json=True,
)

client = Client(
    transport=_transport,
    fetch_schema_from_transport=True,
)

Define and run the GraphQL query

Define the GraphQL to find all works from PANGAEA, DRYAD and Global Biodiversity Information Facility (GBIF) repositories using keywords: polarstern, butterfly and Lake Malawi respectively.

# Generate the GraphQL query
query_params = {
    "pangaea_repository" : "pangaea.repository",
    "pangaea_keyword" : "polarstern",
    "dryad_repository" : "dryad.dryad",
    "dryad_keyword" : "butterfly",
    "gbif_repository" : "gbif.gbif",
    "gbif_keyword" : "Lake Malawi", 
}

query = gql("""query getWorksByRepositoryAndKeyword(
    $pangaea_repository: ID!, $pangaea_keyword: String!,
    $dryad_repository: ID!, $dryad_keyword: String!
    $gbif_repository: ID!, $gbif_keyword: String!
    )
{
  pangaea: repository(id: $pangaea_repository) {
    id
    name
    citationCount
    works(query: $pangaea_keyword) {
      totalCount
      published {
        title
        count
      }
      nodes {
        id
        type
        publicationYear
        bibtex
        titles {
          title
        }
        citationCount
        viewCount
        downloadCount
      }
    }
  },
  dryad: repository(id: $dryad_repository) {
    id
    name
    citationCount
    works(query: $dryad_keyword) {
      totalCount
      published {
        title
        count
      }
      nodes {
        id
        type
        publicationYear
        bibtex
        titles {
          title
        }
        citationCount
        viewCount
        downloadCount
      }
    }
  },
  gbif: repository(id: $gbif_repository) {
    id
    name
    citationCount
    works(query: $gbif_keyword) {
      totalCount
      published {
        title
        count
      }
      nodes {
        id
        type
        publicationYear
        bibtex
        titles {
          title
        }
        citationCount
        viewCount
        downloadCount
      }
    }
  }
}
""")

Run the above query via the GraphQL client

import json
data = client.execute(query, variable_values=json.dumps(query_params))

Display the number of works

For each repository, display the total number of works matching the respective query.

# Get the total number of datasets matching the query
works = {}
for repo in ['pangaea', 'dryad', 'gbif']:
    works[repo] = data[repo]['works']
    print("The number of works for query '%s' in repository %s:\n%s" % (query_params['%s_keyword' % repo], data[repo]['name'], str(works[repo]['totalCount'])))

The number of works for query 'polarstern' in repository PANGAEA:
19616
The number of works for query 'butterfly' in repository DRYAD:
551
The number of works for query 'Lake Malawi' in repository Global Biodiversity Information Facility:
8264

Display the number of citations of the works

For each repository, display the total number of citations of works matching the respective query.

# Get the total number of citations per repository
for repo in ['pangaea', 'dryad', 'gbif']:
    print("The total number of citations for repository %s:\n%s" % (data[repo]['name'], str(data[repo]['citationCount'])))

The total number of citations for repository PANGAEA:
1790058
The total number of citations for repository DRYAD:
28212
The total number of citations for repository Global Biodiversity Information Facility:
13262905

Plot the number of works per year

For each repository, display a bar plot showing the counts of works matching the respective query, across years.

# Plot the total number of datasets to date, by year
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import numpy as np

for repo in ['pangaea', 'dryad','gbif']:
    works = data[repo]['works']
    name = data[repo]['name']
    plt.rcdefaults()
    sorted_years = sorted([int(s['title']) for s in works['published']])
    num_outputs4sorted_years = [s['count'] for s in works['published']]
    # Get a list of all consecutive years between min and max year (inclusive)
    all_years = list(range(sorted_years[0], sorted_years[-1]))
    # Populate output counts (into num_counts) for all consecutive years
    num_outputs = []
    for year in all_years:
        if year in sorted_years:
            idx = sorted_years.index(year)
            num_outputs.append(num_outputs4sorted_years[idx])
        else:
            num_outputs.append(0)     

    fig, ax = plt.subplots(1, 1, figsize = (10, 5))
    x_pos = np.arange(len(all_years))
    ax.bar(x_pos, num_outputs, align='center', color='blue', edgecolor='black', linewidth=1, alpha=0.5)
    ax.set_xticks(x_pos)
    ax.set_xticklabels(all_years, rotation='vertical')
    ax.set_ylabel('Number of works per Year')
    ax.set_xlabel('Year')
    ax.set_title("Number of works retrieved via query '%s' from %s" % (query_params["%s_keyword" % repo], name))
    plt.show()

Display works in tabular format

For each repository and query, display the works in a html table, including the number of their citations, views and downloads.

from IPython.core.display import display, HTML

# Get details for each output
for repo in ['pangaea', 'dryad','gbif']:
    works = data[repo]['works']
    name = data[repo]['name']
    outputs = [['ID','Type','Publication Year','Titles','Number of Citations', 'Number of Views', 'Number of Downloads']]
    for r in works['nodes']:
        id = '<a href="%s">%s</a></html>' % (r['id'], '/'.join(r['id'].split("/")[3:]))
        titles = '; '.join([s['title'] for s in r['titles']])
        output = [id, r['type'], str(r['publicationYear']), titles, str(r['citationCount']), str(r['viewCount']), str(r['downloadCount'])]
        outputs += [output]
    
    # Display outputs as html table 
    html_table = '<html><table><caption><b>"%s" works from %s</b></caption>' % (query_params["%s_keyword" % repo], name)  
    html_table += '<tr><th style="text-align:center;">' + '</th><th style="text-align:center;">'.join(outputs[0]) + '</th></tr>'
    for row in outputs[1:]:
        html_table += '<tr><td style="text-align:left;">' + '</td><td style="text-align:left;">'.join(row) + '</td></tr>'
    html_table += '</table></html>'
    display(HTML(html_table))

"polarstern" works from PANGAEA

ID	Type	Publication Year	Titles	Number of Citations	Number of Views	Number of Downloads
10.1594/pangaea.52064	Dataset	1997	Radionuclides and silicate measured on water bottle samples during POLARSTERN cruise ANT-X/6, supplement to: Friedrich, Jana (1997): Polonium-210 und Blei-210 im Südpolarmeer: Natürliche Tracer für biologische und hydrographische Prozesse im Oberflächenwasser des Antarktischen Zirkumpolarstroms und des Weddellmeeres (Polonium-210 and Lead-210 in the Southern Polar Ocean: Naturally occurring tracers of biological and hydrographical tracers of biological and hydrographical processes in the surface waters of the Antarctic Circumpolar Current and the Weddell Sea). Berichte zur Polarforschung = Reports on Polar Research, 235, 155 pp	0	0	0
10.1594/pangaea.54265	Dataset	1993	Lead and aluminum in Atlantic surface water measured along the track of POLARSTERN cruise ANT-VIII/7 and ANT-IX/1 (Tables 2, 3), supplement to: Helmers, Eckard; Rutgers van der Loeff, Michiel M (1993): Lead and aluminum in Atlantic surface water (50°N to 50°S) reflecting anthropogenic and natural sources in the eolian transport. Journal of Geophysical Research: Oceans, 98(C11), 20261-20273	0	0	0
10.1594/pangaea.54605	Dataset	1997	Organic geochemistry on surface sediments from the Laptev Sea, supplement to: Fahl, Kirsten; Stein, Ruediger (1997): Modern organic carbon deposition in the Laptev Sea and the adjacent continental slope: surface water productivity vs. terrigenous input. Organic Geochemistry, 26(5-6), 379-390	0	0	0
10.1594/pangaea.55750	Dataset	2001	Isotope tracers (Delta δ¹³C) in Weddell Sea water during POLARSTERN cruise ANT-XII/3, supplement to: Mackensen, Andreas (2001): Oxygen and carbon stable isotope tracers of Weddell Sea water masses: new data and some paleoceanographic implications. Deep Sea Research Part I: Oceanographic Research Papers, 48(6), 1401-1422	0	0	0
10.1594/pangaea.56218	Dataset	1999	Lipid composition of particulate matter measured on water bottle samples during POLARSTERN cruise ARK-XI/1, supplement to: Alexandrova, Olga A; Shevchenko, Vladimir P; Fahl, Kirsten; Stein, Ruediger (1999): The lipid composition of particulate matter from the transitional zone between Kara and Laptev Seas. In: Stein, R; Fahl, K; Ivanov, G I; Levitan, M A; Tarasov, G (eds.), Modern and Late Quaternary depositional environment of the St. Anna Trough area, northern Kara Sea, Reports on Polar Research, 244 pp, 342, 93-102	0	0	0
10.1594/pangaea.60056	Dataset	2007	Radionuclides measured in surface water samples from the South Atlantic, supplement to: Hegner, Ernst; Dauelsberg, Hans-Jürgen; Rutgers van der Loeff, Michiel M; Jeandel, Catherine; de Baar, Hein J W (2007): Nd isotopes constrain the origin of suspended particles in the Atlantic sector of the Southern Ocean. Geochemistry, Geophysics, Geosystems, 8, Q10008	0	0	0
10.1594/pangaea.314690	Dataset	2003	Effects of varying food-availability on ecology and distribution of smallest benthic organisms in sediments of the arctic Fram Strait during POLARSTERN cruise ARK-XV/2, supplement to: Schewe, Ingo; Soltwedel, Thomas (2003): Benthic response to ice-edge-induced particle flux in the Arctic Ocean. Polar Biology, 26(9), 610-620	295	0	0
10.1594/pangaea.370797	Dataset	2005	Terrain model of the Håkon Mosby Mud Volcano (10 m grid size), supplement to: Beyer, Andreas; Rathlau, Rike; Schenke, Hans Werner (2005): Multibeam bathymetry of the Håkon Mosby Mud Volcano. Marine Geophysical Research, 26, 61-75	0	0	0
10.1594/pangaea.370808	Dataset	2006	Terrain model of the eastern slope of the Porcupine Seabight (50 m grid size), supplement to: Beyer, Andreas; Schenke, Hans Werner; Klenke, Martin; Niederjasper, Fred (2003): High resolution bathymetry of the eastern slope of the Porcupine Seabight. Marine Geology, 198(1-2), 27-54	0	0	0
10.1594/pangaea.472287	Collection	2006	Standard meteorological measurements on board of POLARSTERN during expedition ANT-IV (PS08, 4 cruises, south summer 1985/86) in the Atlantic and Weddell Sea, Antarctica	0	0	0
10.1594/pangaea.526589	Dataset	2006	Digital terrain model (DTM) of the central Fram Strait, supplement to: Klenke, Martin; Schenke, Hans Werner (2002): A new bathymetric model for the central Fram Strait. Marine Geophysical Research, 23(4), 367-378	0	0	0
10.1594/pangaea.526941	Collection	2006	Mapping of the Eltanin impact area in the South-East Pacific	0	0	0
10.1594/pangaea.536199	Collection	2006	Clay minerals in the Norwegian Sea and Fram Strait, investigation from sediment traps and cores, supplement to: Berner, Heinrich (1991): Mechanismen der Sedimentbildung in der Framstrasse, im Arktischen Ozean und in der Norwegischen See. Berichte aus dem Fachbereich Geowissenschaften der Universität Bremen, 20, 167 pp	0	0	0
10.1594/pangaea.552177	Collection	1990	Sedimentological and paleomagnetic investigations on two gravity cores and ODP sites 113-690 and 693 off Kapp Norvegia, Antarctic continental margin, supplement to: Grobe, Hannes; Fütterer, Dieter K; Spieß, Volkhard (1990): Oligocene to Quaternary sedimentation processes on the Antarctic continental margin, ODP Leg 113, Site 693. In: Barker, PF; Kennett, JP; et al. (eds.), Proceedings of the Ocean Drilling Program, Scientific Results, College Station, TX (Ocean Drilling Program), 113, 121-131	0	0	0
10.1594/pangaea.557786	Collection	2006	AWI Bathymetric Chart of the Fram Strait (BCFS) (Scale 1:100,000)	0	0	0
10.1594/pangaea.610160	Collection	2007	Polyfluorinated alkyl substances (PFAS) in high-volume air samples collected during Polarstern expedition ANT-XXIII/1, supplement to: Jahnke, Annika; Berger, Urs; Ebinghaus, Ralf; Temme, Christian (2007): Latitudinal gradient of airborne polyfluorinated alkyl substances in the marine atmosphere between Germany and South Africa (53° N-33° S). Environmental Science and Technology, 41(9), 3055 -3061	0	0	0
10.1594/pangaea.646297	Collection	2007	Abundance of copepods from multinet samples during POLARSTERN cruise ANT-XXII/2 (ISPOL), supplement to: Schnack-Schiel, Sigrid B; Michels, Jan; Mizdalski, Elke; Schodlok, Michael P; Schröder, Michael (2008): Composition and community structure of zooplankton in the sea ice covered western Weddell Sea in spring 2004 - with emphasis on calanoid copepods. Deep Sea Research Part II: Topical Studies in Oceanography, 55(8-9), 1040-1055	0	0	0
10.1594/pangaea.693818	Collection	1998	Sediments in arctic sea ice - entrainment, characterization and quantification, supplement to: Lindemann, Frank (1998): Sedimente im arktischen Meereis - Eintrag, Charakterisierung und Quantifizierung (Sediments in arctic sea ice - entrainment, characterization and quantification). Berichte zur Polarforschung = Reports on Polar Research, 283, 124 pp	0	0	0
10.1594/pangaea.695350	Collection	2008	Clay mineralogy of sediment cores from the Arctic Ocean, supplement to: Vogt, Christoph; Knies, Jochen (2008): Sediment dynamics in the Eurasian Arctic Ocean during the last deglaciation - The clay mineral group smectite perspective. Marine Geology, 250(3-4), 211-222	0	0	0
10.1594/pangaea.701279	Dataset	2005	Continuous VM-ADCP (vessel-mounted acoustic Doppler current profiler) profiles of horizontal velocities and raw acoustic gain control data during Polarstern cruise ANT-XVIII/2, supplement to: Cisewski, Boris; Strass, Volker H; Prandke, Hartmut (2005): Upper-ocean vertical mixing in the Antarctic Polar Front Zone. Deep Sea Research Part II: Topical Studies in Oceanography, 52(9-10), 1087-1108	0	0	0
10.1594/pangaea.701344	Collection	2001	Sedimentology and age determinations on core PS2813-1, supplement to: Holz, Christine (2001): Glazialmarine Sedimentationsprozesse am Kontinentalhang des westlichen Weddellmeeres. Diploma Thesis, Geologisch-Paläontologisches Institut der Christian-Albrechts-Universität zu Kiel, Germany, 70 pp	0	0	0
10.1594/pangaea.701354	Collection	2008	Heavy mineral investigations from surface sediments of the East Greenland continental margin, supplement to: Grafenauer, Ingo (1998): Terrigener Sedimenteintrag am Ostgrönländischen Kontinentalrand - Rekonstruktion anhand von Schwermineraldaten. Diploma Thesis, Rheinisch-Westfälisch-Technische Hochschule Aachen, 94 pp	0	0	0
10.1594/pangaea.702107	Collection	2010	Physical oceanography, sea-bed photographs and videos of benthos from the Weddell Sea taken with remote operated vehicle CHEROKEE during POLARSTERN cruise ANT-XXIII/8, supplement to: Gutt, Julian; Barratt, Iain; Domack, Eugene W; d'Udekem d'Acoz, Cédric; Dimmler, Werner; Grémare, Antoine; Heilmayer, Olaf; Isla, Enrique; Janussen, Dorte; Jorgensen, Elaina; Kock, Karl-Hermann; Lehnert, Linn Sophia; López-Gonzáles, Pablo José; Langner, Stephanie; Linse, Katrin; Manjón-Cabeza, Maria Eugenia; Meißner, Meike; Montiel, Américo; Raes, Maarten; Robert, Henri; Rose, Armin; Schepisi, Elisabet Sañé; Saucède, Thomas; Scheidat, Meike; Schenke, Hans Werner; Seiler, Jan; Smith, Craig (2011): Biodiversity change after climate-induced ice-shelf collapse in the Antarctic. Deep Sea Research Part II: Topical Studies in Oceanography, 58(1-2), 74-83	0	0	0
10.1594/pangaea.706910	Collection	2005	Properties of foraminifera from Denmark Strait, supplement to: Lorenz, Andrea (2005): Variability of benthic foraminifera north and south of the Denmark Strait. PhD Thesis, Mathematisch-Naturwissenschaftliche Fakultät der Christian-Albrechts-Universität zu Kiel, Germany, 139 pp	0	0	0
10.1594/pangaea.708081	Dataset	1997	AWI Bathymetric Chart of the Weddell Sea, Antarctica (BCWS)	0	0	0

"butterfly" works from DRYAD

ID	Type	Publication Year	Titles	Number of Citations	Number of Views	Number of Downloads
10.5061/dryad.8bb43	Dataset	2011	Data from: UV photoreceptors and UV-yellow wing pigments in Heliconius butterflies allow a color signal to serve both mimicry and intraspecific communication	1	145	43
10.5061/dryad.8bb43/1	Dataset	2011	Reflectance spectra of Heliconiine butterfly yellow wing pigments	0	0	0
10.5061/dryad.8bb43/2	Dataset	2011	Appendix: Wing photomicrographs and averaged yellow reflectance spectra	0	0	0
10.5061/dryad.rg4dc36s	Dataset	2011	Data from: Evidence for evolutionary change associated with the recent range expansion of the British butterfly, Polyommatus agestis, in response to climate change	1	13	5
10.5061/dryad.1705/1	Dataset	2010	Napeogenesspecies.xml	0	0	0
10.5061/dryad.1705/4	Dataset	2010	Ithomiaspecies.xml	0	0	0
10.5061/dryad.1540	Dataset	2010	Data from: Rapid microsatellite isolation from a butterfly by de novo transcriptome sequencing: performance and a comparison with AFLP-derived distances	1	245	53
10.5061/dryad.1705	Dataset	2010	Data from: Out of the Andes: patterns of diversification in clearwing butterflies	1	189	53
10.5061/dryad.1276	Dataset	2010	Data from: Allopatric origin of cryptic butterfly species that were discovered feeding on distinct host plants in sympatry	1	88	11
10.5061/dryad.615	Dataset	2009	Data from: A partitioned likelihood analysis of swallowtail butterfly phylogeny (Lepidoptera: Papilionidae)	1	112	11
10.5061/dryad.cb6pk	Dataset	2011	Data from: Similarity and specialization of the larval versus adult diet of European butterflies and moths	1	179	68
10.5061/dryad.f0b2f083	Dataset	2012	Data from: Genomic regions with a history of divergent selection affect fitness of hybrids between two butterfly species	1	127	44
10.5061/dryad.vj86fq35	Dataset	2012	Data from: Response to selection on cold tolerance is constrained by inbreeding	1	82	17
10.5061/dryad.d56g84n5	Dataset	2012	Data from: Use of an exotic host plant affects mate choice in an insect herbivore	1	61	13
10.5061/dryad.s672t4gh	Dataset	2012	Data from: Dispersal and gene flow in the rare, parasitic Large Blue butterfly Maculinea arion	1	67	21
10.5061/dryad.7nn6p	Dataset	2012	Data from: The evolution of alternative developmental pathways: footprints of selection on life-history traits in a butterfly	1	100	14
10.5061/dryad.82j66	Dataset	2012	Data from: Food plant-derived disease tolerance and resistance in a natural butterfly-plant-parasite interaction	1	80	23
10.5061/dryad.65r9c	Dataset	2012	Data from: Does selection on increased cold tolerance in the adult stage confer resistance throughout development?	1	51	20
10.5061/dryad.m27qq	Dataset	2012	Data from: Butterfly genome reveals promiscuous exchange of mimicry adaptations among species	0	58	18
10.5061/dryad.c7f1f	Dataset	2012	Data from: A range-wide genetic bottleneck overwhelms landscape heterogeneity and local abundance in shaping genetic patterns of an alpine butterfly (Lepidoptera: Pieridae: Colias behrii)	1	59	17
10.5061/dryad.284gk	Dataset	2012	Data from: Ecological constraints on female fitness in a phytophagous insect	1	64	19
10.5061/dryad.5rm2f	Dataset	2012	Data from: Environmental risk assessment for the small tortoiseshell Aglais urticae and a stacked Bt-maize with combined resistances against Lepidoptera and Chrysomelidae in central European agrarian landscapes	1	115	14
10.5061/dryad.f76f3	Dataset	2013	Data from: Transcriptome analysis reveals novel patterning and pigmentation genes underlying Heliconius butterfly wing pattern variation	1	89	33
10.5061/dryad.733d9	Dataset	2012	Data from: Dissecting the contributions of plasticity and local adaptation to the phenology of a butterfly and its host plants	1	70	18
10.5061/dryad.cr17v	Dataset	2012	Data from: Sharp genetic discontinuity across a unimodal Heliconius hybrid zone	1	216	44

"Lake Malawi" works from Global Biodiversity Information Facility

ID	Type	Publication Year	Titles	Number of Citations	Number of Views	Number of Downloads
10.15468/n6ftyd	Dataset	2019	rmca-albertine-rift-cichlids	206	0	0
10.15468/dl.fk6ozu	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.sdaj2e	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.pyr4ax	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.yj2she	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.t1aupt	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.iad1t6	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.fujlga	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/dl.oqdmcc	Dataset	2015	GBIF Occurrence Download	0	0	0
10.15468/lvyqrq	Dataset	2016	Royal Museum of Central Africa - Albertian Rift Cichlids (ENBI wp13)	0	0	0
10.15468/q3e6mn	Dataset	2006	Astiotrema turneri n. sp. (Digenea: Plagiorchiidae) from cichlid fishes (Cichlidae: Perciformes) of Lake Malawi, south­eastern Africa	0	0	0
10.15468/dl.xucfgy	Dataset	2016	GBIF Occurrence Download	0	0	0
10.15468/x0wrhl	Dataset	2006	Astiotrema turneri n. sp. (Digenea: Plagiorchiidae) from cichlid fishes (Cichlidae: Perciformes) of Lake Malawi, south­eastern Africa	0	0	0
10.15468/dl.zvv38t	Dataset	2016	GBIF Occurrence Download	0	0	0
10.15468/dl.shgios	Dataset	2016	GBIF Occurrence Download	0	0	0
10.15468/dl.obxf4e	Dataset	2016	GBIF Occurrence Download	0	0	0
10.15468/dl.pej1nt	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.ezc6o3	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.pu4oky	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.szsg5c	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.zmysjk	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.1pajgb	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.gsfp2r	Dataset	2017	GBIF Occurrence Download	0	0	0
10.15468/dl.jdlxgx	Dataset	2018	GBIF Occurrence Download	0	0	0
10.15468/dl.govpwq	Dataset	2018	GBIF Occurrence Download	0	0	0

Download works in BibTeX format

Download the works in a single BibTeX file per repository

import pandas as pd
from IPython.display import Javascript
from requests.utils import requote_uri

# For each repository, download a file of BibTeX entries in csv format
for repo in ['pangaea', 'dryad','gbif']:
    works = data[repo]['works']
    bibtex_data = []
    for r in works['nodes']:
        bibtex_data.append([r['bibtex']])
    df = pd.DataFrame(bibtex_data, columns = None)
    
    js_download = """
var csv = '%s';

var filename = '%s_%s.bib';
var blob = new Blob([csv], { type: 'application/x-bibtex;charset=utf-8;' });
if (navigator.msSaveBlob) { // IE 10+
    navigator.msSaveBlob(blob, filename);
} else {
    var link = document.createElement("a");
    if (link.download !== undefined) { // feature detection
        // Browsers that support HTML5 download attribute
        var url = URL.createObjectURL(blob);
        link.setAttribute("href", url);
        link.setAttribute("download", filename);
        link.style.visibility = 'hidden';
        document.body.appendChild(link);
        link.click();
        document.body.removeChild(link);
    }
}
""" % (df.to_csv(index=False, header=False).replace('\n','\\n').replace("\'","\\'").replace("\"","").replace("\r",""), query_params["%s_repository" % repo], requote_uri(query_params["%s_keyword" % repo]))

# display(Javascript(js_download))

Define and run GraphQL query to retrieve citations for a single work

The query will retrieve citations for IUCN Red List assessment occurrence data for freshwater species native to the Lake Malawi/Nyasa/Niassa Catchment.

# Generate the GraphQL query: Get citations for a specific work from the repository
citations_query_params = {
    "id" : "https://doi.org/10.15468/1z5fn8",
    "maxCitations" : 75
}

citation_query = gql("""query getCitationsByWorkId($id: ID!, $maxCitations: Int!)
{
  work(id: $id) {
    id
    titles {
      title
    }
    type
    publicationYear
    citations(first: $maxCitations) {
      totalCount
      nodes {
        id
        type
        publicationYear
        repository {
          id
          name
        }
        titles {
          title
        }
        bibtex
        citationCount
        viewCount
        downloadCount
      }
    }
  }
}
""")

Run the above query

import json
citations = client.execute(citation_query, variable_values=json.dumps(citations_query_params))

Display the number of citations

Display the number of citations of IUCN Red List assessment occurrence data for freshwater species native to the Lake Malawi/Nyasa/Niassa Catchment.

# Get the total number of citations matching the query
citations_data = citations['work']['citations']
print("The number of citations for work %s:\n%s" % (citations_query_params["id"], str(citations_data['totalCount'])))

The number of citations for work https://doi.org/10.15468/1z5fn8:
2188

Display citations in tabular format

Display citations of IUCN Red List assessment occurrence data for freshwater species native to the Lake Malawi/Nyasa/Niassa Catchment in a html table, including the number of their respective citations, views and downloads.

from IPython.core.display import display, HTML

# Get details for each citation
outputs = [['ID','Type','Publication Year','Titles','Number of Citations', 'Number of Views', 'Number of Downloads']]
for r in citations_data['nodes']:
    citation_id = '<a href="%s">%s</a></html>' % (r['id'], '/'.join(r['id'].split("/")[3:]))
    titles = '; '.join([s['title'] for s in r['titles']])
    output = [citation_id, r['type'], str(r['publicationYear']), titles, str(r['citationCount']), str(r['viewCount']), str(r['downloadCount'])]
    outputs += [output]
    
# Display outputs as html table
id_href = '<a href="%s">%s</a></html>' % (citations_query_params['id'], '/'.join(citations_query_params['id'].split("/")[3:]))
html_table = '<html><table><caption><b>Citations of %s from %s</b></caption>' % (id_href, query_params["%s_repository" % "gbif"] )  
html_table += '<tr><th style="text-align:center;">' + '</th><th style="text-align:center;">'.join(outputs[0]) + '</th></tr>'
for row in outputs[1:]:
    html_table += '<tr><td style="text-align:left;">' + '</td><td style="text-align:left;">'.join(row) + '</td></tr>'
html_table += '</table></html>'
display(HTML(html_table))

Citations of 10.15468/1z5fn8 from gbif.gbif

ID	Type	Publication Year	Titles	Number of Citations	Number of Views	Number of Downloads
10.15468/dl.iftsjs	Dataset	2019	GBIF Occurrence Download	1	0	0
10.15468/dl.ezobqv	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.oyiynl	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.yjeevx	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.pltnoo	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.nfglle	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.2orexs	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.3c0gsw	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.qzsifz	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.8w8vv3	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.v2sxry	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.gg19ne	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.urhked	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.8hxedw	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.jpdcpu	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.bajigq	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.wywzy9	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.yqae0u	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.dxd0qy	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.w83wnc	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.6t0umr	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.yf745q	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.esynpz	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.we2dps	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.hzxgzz	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.6gquca	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.qlykpv	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.sk612y	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.7geyav	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.it1jrj	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.ye4kxz	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.jo7t9z	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.q8ials	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.enfztm	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.eyd7xn	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.87fxhr	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.uv7pr6	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.faelkn	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.fcvx6c	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.rlt9az	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.lxvdyv	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.m9zvcx	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.abkcmp	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.aj0tpg	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.prbfrl	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.afrrsr	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.szzefa	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.ezsiaf	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.vendg1	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.ocvv3r	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.adsito	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.1cmlm6	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.5j9gwo	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.jwfh5m	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.mp7bjf	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.0tvtwm	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.t2orua	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.pjz1kl	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.qhzm3x	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.puhkd6	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.yeyajj	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.m9b6vj	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.lwa0xt	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.ovhlav	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.9dxs2k	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.5jowpk	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.bjcahf	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.zkjptx	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.izj3rx	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.nobyv8	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.b4ngf4	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.fhealt	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.9ee5c7	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.mf7fg2	Dataset	2019	GBIF Occurrence Download	0	0	0
10.15468/dl.kelhh6	Dataset	2019	GBIF Occurrence Download	0	0	0

Download citations in BibTeX format

Download the citations of IUCN Red List assessment occurrence data for freshwater species native to the Lake Malawi/Nyasa/Niassa Catchment in a single BibTeX file.

import pandas as pd
from IPython.display import Javascript
from requests.utils import requote_uri

# Download a file of BibTeX entries in csv format, for the citations of citations_query_params['id']
for r in works['nodes']:
    bibtex_data = []
    for r in works['nodes']:
        bibtex_data.append([r['bibtex']])
    df = pd.DataFrame(bibtex_data, columns = None)
id_label = '/'.join(citations_query_params['id'].split("/")[3:])

js_download = """
var csv = '%s';
var filename = '%s.bib';
var blob = new Blob([csv], { type: 'application/x-bibtex;charset=utf-8;' });
if (navigator.msSaveBlob) { // IE 10+
    navigator.msSaveBlob(blob, filename);
} else {
    var link = document.createElement("a");
    if (link.download !== undefined) { // feature detection
        // Browsers that support HTML5 download attribute
        var url = URL.createObjectURL(blob);
        link.setAttribute("href", url);
        link.setAttribute("download", filename);
        link.style.visibility = 'hidden';
        document.body.appendChild(link);
        link.click();
        document.body.removeChild(link);
    }
}
""" % (df.to_csv(index=False, header=False).replace('\n','\\n').replace("\'","\\'").replace("\"","").replace("\r",""), requote_uri(id_label))
    
# display(Javascript(js_download))