BASIC JOURNAL ANALYSIS#

The notebook uses frozen static metadata files from the summer of 2022 to analyze the journal-level importance of EarthCube data.

It broadly aims to explore:

the proportion of EC papers that have received more citations than the average paper for the journal/year;
the cumulative citation count for this group and whether they are higher than the sum of all of these averages;
the top 10 papers in terms of % above the average for their journal/year

Fixed inputs to this notebook are:

../inputs/20220805_ec_journal_titles_plus_citations.xlsx: the journal citation exported data from Web of Science
../inputs/cr_metadata_20220610012125.json: we use a fixed snapshop metadata file extracted from crossref on June 10, 2022.

NOTE: We have fixed input snapshot targets because both sources are changing and updating at different times. Furthermore, WOS data requires subscription, which makes dynamic replication difficult.

import pandas as pd
import json

df = pd.read_excel("../inputs/20220805_ec_journal_titles_plus_citations.xlsx")

df

	Unnamed: 0	doi	journal_title	publication_title	url	Year	Journal_Avg_cit
0	15	10.5065/p2jj-9878	--	--	https://doi.org/10.5065/p2jj-9878	NaN	NI
1	79	10.1594/ieda/100709	--	--	https://doi.org/10.1594/ieda/https://doi.org/1...	NaN	NI
2	81	10.5281/zenodo.5496306	--	--	https://doi.org/10.5281/zenodo.5496306	NaN	NI
3	93	10.13140/rg.2.1.4908.4561	--	--	https://doi.org/10.13140/rg.2.1.4908.4561	NaN	NI
4	109	10.6084/m9.figshare.4272164.v1	--	--	https://doi.org/10.6084/m9.figshare.4272164.v1	NaN	NI
...	...	...	...	...	...	...	...
236	102	10.1111/tgis.12233	Transactions in GIS	Crowdsensing smart ambient environments and se...	https://doi.org/10.1111/tgis.12233	2016.0	13.08
237	219	10.1002/2015wr017342	Water Resources Research	Hydrocomplexity: Addressing water security and...	https://doi.org/10.https://doi.org/10.2/2015wr...	2015.0	40.97
238	27	10.22498/pages	NaN	Past Global Changes Magazine	https://doi.org/10.22498/pages	NaN	NI
239	60	10.17504/protocols.io.fjjbkkn	NaN	ECOGEO 'Omics Training: Introduction to Enviro...	https://doi.org/10.17504/protocols.io.fjjbkkn	NaN	NI
240	222	10.1101/647651	NaN	Ecological and genomic attributes of novel bac...	https://doi.org/10.1https://doi.org/10./647651	NaN	NI

241 rows × 7 columns

df['Journal_Avg_cit'] = df['Journal_Avg_cit'].astype(float, errors='ignore')

df

	Unnamed: 0	doi	journal_title	publication_title	url	Year	Journal_Avg_cit
0	15	10.5065/p2jj-9878	--	--	https://doi.org/10.5065/p2jj-9878	NaN	NI
1	79	10.1594/ieda/100709	--	--	https://doi.org/10.1594/ieda/https://doi.org/1...	NaN	NI
2	81	10.5281/zenodo.5496306	--	--	https://doi.org/10.5281/zenodo.5496306	NaN	NI
3	93	10.13140/rg.2.1.4908.4561	--	--	https://doi.org/10.13140/rg.2.1.4908.4561	NaN	NI
4	109	10.6084/m9.figshare.4272164.v1	--	--	https://doi.org/10.6084/m9.figshare.4272164.v1	NaN	NI
...	...	...	...	...	...	...	...
236	102	10.1111/tgis.12233	Transactions in GIS	Crowdsensing smart ambient environments and se...	https://doi.org/10.1111/tgis.12233	2016.0	13.08
237	219	10.1002/2015wr017342	Water Resources Research	Hydrocomplexity: Addressing water security and...	https://doi.org/10.https://doi.org/10.2/2015wr...	2015.0	40.97
238	27	10.22498/pages	NaN	Past Global Changes Magazine	https://doi.org/10.22498/pages	NaN	NI
239	60	10.17504/protocols.io.fjjbkkn	NaN	ECOGEO 'Omics Training: Introduction to Enviro...	https://doi.org/10.17504/protocols.io.fjjbkkn	NaN	NI
240	222	10.1101/647651	NaN	Ecological and genomic attributes of novel bac...	https://doi.org/10.1https://doi.org/10./647651	NaN	NI

241 rows × 7 columns

df_subset = df[df['Journal_Avg_cit']!='NI']

df_subset[df_subset['Journal_Avg_cit']>30]

	Unnamed: 0	doi	journal_title	publication_title	url	Year	Journal_Avg_cit
45	134	10.1145/3129246	ACM Transactions on Database Systems	EmptyHeaded	https://doi.org/10.1145/3129246	2017.0	35.65
53	7	10.1175/bams-d-14-00164.1	Bulletin of the American Meteorological Society	The Earth System Prediction Suite: Toward a Co...	https://doi.org/10.1175/bams-d-14-00164.1	2016.0	30.85
54	65	10.1175/bams-d-15-00239.1	Bulletin of the American Meteorological Society	Sharing Experiences and Outlook on Coupling Te...	https://doi.org/10.1175/bams-d-15-00239.1	2016.0	30.85
70	147	10.1007/s40641-018-0107-0	Current Climate Change Reports	Rising Oceans Guaranteed: Arctic Land Ice Loss...	https://doi.org/10.https://doi.org/10.7/s40641...	2018.0	43
109	106	10.1186/s13073-015-0202-y	Genome Medicine	Use of semantic workflows to enhance transpare...	https://doi.org/10.1186/s13073-015-0202-y	2015.0	42.88
110	234	10.1111/gfl.12114	Geofluids	DigitalCrust - a 4D data system of material pr...	https://doi.org/10.1111/gfl.12114	2015.0	30.45
133	82	10.1109/tgrs.2014.2382566	IEEE Transactions on Geoscience and Remote Sen...	Regular Shape Similarity Index: A Novel Index ...	https://doi.org/10.1https://doi.org/10./tgrs.2...	2015.0	43.05
178	225	10.1038/nbt.4306	Nature Biotechnology	Minimum Information about an Uncultivated Viru...	https://doi.org/10.https://doi.org/10.8/nbt.4306	2019.0	67.85
179	47	10.1038/s41561-018-0272-8	Nature Geoscience	Similarity of fast and slow earthquakes illumi...	https://doi.org/10.https://doi.org/10.8/s41561...	2019.0	39.7
206	92	10.1016/j.renene.2017.02.052	Renewable Energy	Short-term photovoltaic power forecasting usin...	https://doi.org/10.https://doi.org/10.6/j.rene...	2017.0	32.19
207	44	10.1126/science.aad7048	Science	Liberating field science samples and data	https://doi.org/10.1126/science.aad7048	2016.0	87
208	190	10.1126/science.342.6162.1041-b	Science	Open Data: Crediting a Culture of Cooperation	https://doi.org/10.1126/science.342.6162.https...	2013.0	126.97
209	180	10.1038/sdata.2017.88	Scientific Data	A global multiproxy database for temperature r...	https://doi.org/10.https://doi.org/10.8/sdata....	2017.0	39.61
237	219	10.1002/2015wr017342	Water Resources Research	Hydrocomplexity: Addressing water security and...	https://doi.org/10.https://doi.org/10.2/2015wr...	2015.0	40.97

df_json = pd.read_json("../inputs/cr_metadata_20220610012125.json").T

df_json.columns

Index(['indexed', 'reference-count', 'publisher', 'content-domain',
       'short-container-title', 'published-print', 'DOI', 'type', 'created',
       'source', 'is-referenced-by-count', 'title', 'prefix', 'author',
       'member', 'event', 'container-title', 'original-title', 'link',
       'deposited', 'score', 'resource', 'subtitle', 'short-title', 'issued',
       'references-count', 'URL', 'relation', 'published', 'issue', 'license',
       'funder', 'update-policy', 'volume', 'published-online', 'reference',
       'language', 'journal-issue', 'alternative-id', 'archive', 'ISSN',
       'issn-type', 'subject', 'assertion', 'abstract', 'page',
       'published-other', 'accepted', 'publisher-location', 'editor',
       'article-number', 'posted', 'subtype', 'isbn-type', 'ISBN',
       'institution', 'group-title'],
      dtype='object')

df_journals = df.merge(
    df_json[['DOI', 'is-referenced-by-count']].reset_index().drop('index',axis=1).rename(columns={'DOI': 'doi'}),
    on='doi'
)

Journal Citation Counts vs Actual Citation Counts#

Here our interest will turn to how well the actual citation counts match the expected journal citation counts from WOS.

df_journal_cva = df_journals.query('Journal_Avg_cit != "NI"') \
    [["Journal_Avg_cit", "Year", "journal_title", "is-referenced-by-count", 'doi']]

df_journal_cva['Journal_Avg_cit'] = df_journal_cva['Journal_Avg_cit'].astype(int) # note tjos rounds u
df_journal_cva['is-referenced-by-count'] = df_journal_cva['is-referenced-by-count'].astype(int)

df_journal_cva.describe()

	Journal_Avg_cit	Year	is-referenced-by-count
count	150.000000	150.000000	150.000000
mean	14.960000	2018.500000	24.173333
std	15.534279	2.071701	47.443042
min	0.000000	2013.000000	0.000000
25%	5.000000	2017.000000	4.000000
50%	12.000000	2019.000000	10.000000
75%	20.750000	2020.000000	21.750000
max	126.000000	2022.000000	344.000000

df_journal_cva[df_journal_cva['is-referenced-by-count']>=df_journal_cva['Journal_Avg_cit']]

	Journal_Avg_cit	Year	journal_title	is-referenced-by-count	doi
32	35	2017.0	ACM Transactions on Database Systems	43	10.1145/3129246
33	3	2018.0	AI Magazine	13	10.1609/aimag.v39i3.2816
34	0	2022.0	Applied Geochemistry	0	10.1016/j.apgeochem.2022.105273
38	9	2020.0	Biogeosciences	14	10.5194/bg-17-2537-2020
39	19	2018.0	BioScience	77	10.1093/biosci/biy068
...	...	...	...	...	...
211	9	2020.0	The Astrophysical Journal	22	10.3847/1538-4357/aba8a6
215	3	2021.0	The Astrophysical Journal	4	10.3847/1538-4357/abf2c8
217	0	2022.0	The Cryosphere	0	10.5194/tc-16-1431-2022
222	13	2016.0	Transactions in GIS	31	10.1111/tgis.12232
223	13	2016.0	Transactions in GIS	16	10.1111/tgis.12233

79 rows × 5 columns

df_journal_cva[['Journal_Avg_cit', 'Year', 'journal_title']].sort_values(by='journal_title')

	Journal_Avg_cit	Year	journal_title
32	35	2017.0	ACM Transactions on Database Systems
33	3	2018.0	AI Magazine
34	0	2022.0	Applied Geochemistry
35	2	2021.0	Atmospheric Measurement Techniques
39	19	2018.0	BioScience
...	...	...	...
217	0	2022.0	The Cryosphere
223	13	2016.0	Transactions in GIS
222	13	2016.0	Transactions in GIS
224	40	2015.0	Water Resources Research
163	17	2019.0	mBio

150 rows × 3 columns

JOURNAL ANALYSIS#

what is the average journal citation count (over all years)
grouping by journal what are the paper averages

journals = df_journal_cva['journal_title'].unique()

df_journal_cva[['Journal_Avg_cit', 'journal_title']].groupby('journal_title').mean()

	Journal_Avg_cit
journal_title
ACM Transactions on Database Systems	35.0
AI Magazine	3.0
Applied Geochemistry	0.0
Atmospheric Measurement Techniques	2.0
BioScience	19.0
...	...
The Astrophysical Journal Supplement Series	17.0
The Cryosphere	0.0
Transactions in GIS	13.0
Water Resources Research	40.0
mBio	17.0

89 rows × 1 columns

df_journal_cva[['Journal_Avg_cit', 'journal_title']].groupby('journal_title').count().sort_values(by='Journal_Avg_cit', ascending=False)

	Journal_Avg_cit
journal_title
The Astrophysical Journal	12
Environmental Modelling & Software	8
ISPRS International Journal of Geo-Information	6
Computers & Geosciences	5
Journal of Geophysical Research: Space Physics	5
...	...
International Journal of Digital Earth	1
International Journal of Remote Sensing	1
International Journal of Semantic Computing	1
Journal of Atmospheric and Oceanic Technology	1
mBio	1

89 rows × 1 columns

df_journal_density_counts = \
    df_journal_cva[['Journal_Avg_cit', 'journal_title']]\
    .groupby('journal_title').count()\
    .sort_values(by='Journal_Avg_cit', ascending=False)
df_journal_density_counts = df_journal_density_counts.rename(columns={'Journal_Avg_cit': 'paper_counts'})

df_journal_density_counts

	paper_counts
journal_title
The Astrophysical Journal	12
Environmental Modelling & Software	8
ISPRS International Journal of Geo-Information	6
Computers & Geosciences	5
Journal of Geophysical Research: Space Physics	5
...	...
International Journal of Digital Earth	1
International Journal of Remote Sensing	1
International Journal of Semantic Computing	1
Journal of Atmospheric and Oceanic Technology	1
mBio	1

89 rows × 1 columns

df_journal_density_means = \
    df_journal_cva[['Journal_Avg_cit', 'journal_title']].groupby('journal_title').mean().sort_values(by='Journal_Avg_cit', ascending=False)

df_journal_density_means = df_journal_density_means.rename(columns={'Journal_Avg_cit': 'mean_cites'})

df_journal_density_means

	mean_cites
journal_title
Science	106.5
Nature Biotechnology	67.0
Current Climate Change Reports	43.0
IEEE Transactions on Geoscience and Remote Sensing	43.0
Genome Medicine	42.0
...	...
Engaging Science, Technology, and Society	0.0
Remote Sensing in Ecology and Conservation	0.0
The Cryosphere	0.0
Data in Brief	0.0
Applied Geochemistry	0.0

89 rows × 1 columns

For all journals with 2 or more papers, how did they do:

df_journal_cva.columns

Index(['Journal_Avg_cit', 'Year', 'journal_title', 'is-referenced-by-count',
       'doi'],
      dtype='object')

JOURNALS WITH MORE THAN 1 PUBLICATION#

journal_list = df_journal_density_counts[df_journal_density_counts['paper_counts']>1].index.to_list()

df_journal_ec_mean_analysis = \
    df_journal_cva[df_journal_cva['journal_title'].isin(journal_list)]\
        .groupby('journal_title').mean() \
        .drop(columns=['Year']) \
        .merge(df_journal_density_counts, left_index=True, right_index=True) \
        .sort_values('paper_counts', ascending=False) \
        .rename(columns={'Journal_Avg_cit': 'journal_mean_cites', 'is-referenced-by-count': 'ec_mean_cites'})

df_journal_ec_mean_analysis.describe()

C:\Users\kmaull\AppData\Local\Temp\ipykernel_197240\944483266.py:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df_journal_cva[df_journal_cva['journal_title'].isin(journal_list)]\

	journal_mean_cites	ec_mean_cites	paper_counts
count	27.000000	27.000000	27.000000
mean	15.389198	22.391975	3.259259
std	19.555807	33.431244	2.297031
min	1.000000	0.500000	2.000000
25%	7.450000	8.600000	2.000000
50%	12.000000	11.500000	2.000000
75%	17.062500	23.875000	3.000000
max	106.500000	175.000000	12.000000

df_journal_ec_mean_analysis.paper_counts.sum()

88 papers (58.67%) with 2 or more papers in a single journal (27 distinct journals)

df_journal_ec_mean_analysis

	journal_mean_cites	ec_mean_cites	paper_counts
journal_title
The Astrophysical Journal	10.833333	9.166667	12
Environmental Modelling & Software	17.125000	11.500000	8
ISPRS International Journal of Geo-Information	9.166667	10.500000	6
Journal of Geophysical Research: Space Physics	7.400000	10.800000	5
Computers & Geosciences	8.400000	8.200000	5
JAWRA Journal of the American Water Resources Association	13.750000	24.250000	4
Bulletin of the American Meteorological Society	28.333333	34.666667	3
Concurrency and Computation: Practice and Experience	1.000000	2.000000	3
Earth and Space Science	15.333333	34.000000	3
Journal of Proteome Research	7.666667	17.666667	3
Future Generation Computer Systems	17.666667	7.666667	3
Journal of Geophysical Research: Solid Earth	5.333333	4.666667	3
Semantic Web	12.500000	13.000000	2
Science	106.500000	30.000000	2
Remote Sensing	13.500000	4.000000	2
Journal of Hydroinformatics	12.000000	10.000000	2
Geophysical Research Letters	17.000000	175.000000	2
GigaScience	20.000000	17.500000	2
Geosphere	5.000000	6.000000	2
Geophysical Journal International	6.000000	10.500000	2
Geology	22.000000	41.500000	2
Frontiers in Microbiology	8.500000	11.500000	2
Frontiers in Astronomy and Space Sciences	2.000000	0.500000	2
Earth Science Informatics	7.500000	16.500000	2
Computing in Science & Engineering	1.000000	9.000000	2
Computers, Environment and Urban Systems	27.000000	61.000000	2
Transactions in GIS	13.000000	23.500000	2

df_journal_ec_mean_analysis[ 
    df_journal_ec_mean_analysis.ec_mean_cites>=df_journal_ec_mean_analysis.journal_mean_cites
].paper_counts.sum()

47 of 88 papers (53.4%) with a mean_cite greater than or equal to journal_mean_cite (for all years, for journals where there were more than 1 publication)

JOURNALS WITH ONLY 1 PUBLICATION#

journal_list = df_journal_density_counts[df_journal_density_counts['paper_counts']<2].index.to_list()

df_journal_ec_mean_analysis = \
    df_journal_cva[df_journal_cva['journal_title'].isin(journal_list)]\
        .groupby('journal_title').mean() \
        .drop(columns=['Year']) \
        .merge(df_journal_density_counts, left_index=True, right_index=True) \
        .sort_values('paper_counts', ascending=False) \
        .rename(columns={'Journal_Avg_cit': 'journal_mean_cites', 'is-referenced-by-count': 'ec_mean_cites'})

df_journal_ec_mean_analysis.describe()

C:\Users\kmaull\AppData\Local\Temp\ipykernel_197240\3259818779.py:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df_journal_cva[df_journal_cva['journal_title'].isin(journal_list)]\

	journal_mean_cites	ec_mean_cites	paper_counts
count	62.000000	62.000000	62.0
mean	16.370968	32.387097	1.0
std	13.698431	56.452027	0.0
min	0.000000	0.000000	1.0
25%	6.250000	3.250000	1.0
50%	12.500000	13.500000	1.0
75%	23.000000	38.500000	1.0
max	67.000000	344.000000	1.0

62 of 150 papers with only 1 publication in a journal
journal means = 16.37, earthcube paper means = 32.39
stdevs are very different (a lot more variance)

df_journal_ec_mean_analysis

	journal_mean_cites	ec_mean_cites	paper_counts
journal_title
ACM Transactions on Database Systems	35.0	43.0	1
PLoS ONE	26.0	54.0	1
Journal of Climate	14.0	12.0	1
Journal of Environmental Quality	21.0	73.0	1
Journal of Sedimentary Research	10.0	4.0	1
...	...	...	...
Hydrological Processes	21.0	16.0	1
IEEE Systems Journal	25.0	5.0	1
IEEE Transactions on Geoscience and Remote Sensing	43.0	23.0	1
IEEE Transactions on Parallel and Distributed Systems	24.0	2.0	1
mBio	17.0	55.0	1

62 rows × 3 columns

df_journal_ec_mean_analysis[ 
    df_journal_ec_mean_analysis.ec_mean_cites>=df_journal_ec_mean_analysis.journal_mean_cites
].paper_counts.sum()

df_journal_ec_mean_analysis[ 
    df_journal_ec_mean_analysis.ec_mean_cites>df_journal_ec_mean_analysis.journal_mean_cites
].paper_counts.sum()

26 of 62 (41.9%) EC papers produce more than mean citations than the journal means
32 of 62 (51.6%) EC papers produce at least the mean citations from the journal means

the top 10 papers in terms of % above the average for their journal/year

df_journal_cva

	Journal_Avg_cit	Year	journal_title	is-referenced-by-count	doi
32	35	2017.0	ACM Transactions on Database Systems	43	10.1145/3129246
33	3	2018.0	AI Magazine	13	10.1609/aimag.v39i3.2816
34	0	2022.0	Applied Geochemistry	0	10.1016/j.apgeochem.2022.105273
35	2	2021.0	Atmospheric Measurement Techniques	0	10.5194/amt-14-6917-2021
38	9	2020.0	Biogeosciences	14	10.5194/bg-17-2537-2020
...	...	...	...	...	...
216	17	2020.0	The Astrophysical Journal Supplement Series	2	10.3847/1538-4365/aba4aa
217	0	2022.0	The Cryosphere	0	10.5194/tc-16-1431-2022
222	13	2016.0	Transactions in GIS	31	10.1111/tgis.12232
223	13	2016.0	Transactions in GIS	16	10.1111/tgis.12233
224	40	2015.0	Water Resources Research	34	10.1002/2015wr017342

150 rows × 5 columns

df_journal_cva_reindexed = df_journal_cva.rename(columns={
    'Journal_Avg_cit': 'journal_mean_cites',
    'Year': 'year',
    'is-referenced-by-count': 'ec_cites'
    }).reset_index().drop('index', axis=1)
df_journal_cva_reindexed

	journal_mean_cites	year	journal_title	ec_cites	doi
0	35	2017.0	ACM Transactions on Database Systems	43	10.1145/3129246
1	3	2018.0	AI Magazine	13	10.1609/aimag.v39i3.2816
2	0	2022.0	Applied Geochemistry	0	10.1016/j.apgeochem.2022.105273
3	2	2021.0	Atmospheric Measurement Techniques	0	10.5194/amt-14-6917-2021
4	9	2020.0	Biogeosciences	14	10.5194/bg-17-2537-2020
...	...	...	...	...	...
145	17	2020.0	The Astrophysical Journal Supplement Series	2	10.3847/1538-4365/aba4aa
146	0	2022.0	The Cryosphere	0	10.5194/tc-16-1431-2022
147	13	2016.0	Transactions in GIS	31	10.1111/tgis.12232
148	13	2016.0	Transactions in GIS	16	10.1111/tgis.12233
149	40	2015.0	Water Resources Research	34	10.1002/2015wr017342

150 rows × 5 columns

# filter on items that ec beats (or is equal to) the journal mean
df_journal_cva_reindexed[
    df_journal_cva_reindexed.ec_cites>=df_journal_cva_reindexed.journal_mean_cites
]

# cast year as int
df_journal_cva_reindexed.year = df_journal_cva_reindexed.year.astype(int)

# norm the pct diff (i.e. 200% = "100% more")
df_journal_cva_reindexed['ec_pct_diff'] = \
    100*((df_journal_cva_reindexed.ec_cites / df_journal_cva_reindexed.journal_mean_cites) - 1)

top 20 Earthcube papers by pct diff from journal mean

df_tmp = df_journal_cva_reindexed.sort_values(by='ec_pct_diff', ascending=False) \
    .query("journal_mean_cites > 0")[:20]
# df_tmp.doi = df_tmp.doi.apply(lambda d: f"[{d}](https://doi.org/{d})")
df_tmp.columns = ['Journal Mean Cites', 'Publication Year', 'Journal Title', 'EC Cites', 'EC Publication DOI', '% diff mean cites']
df_tmp.to_csv("../outputs/ec_journals_top_pct_delta.csv")

df_tmp

	Journal Mean Cites	Publication Year	Journal Title	EC Cites	EC Publication DOI	% diff mean cites
74	17	2017	International Journal of Digital Earth	344	10.1080/17538947.2016.1239771	1923.529412
61	25	2017	Geophysical Research Letters	336	10.1002/2017gl074954	1244.000000
117	10	2018	Quaternary Research	120	10.1017/qua.2017.105	1100.000000
18	1	2021	Computing in Science & Engineering	10	10.1109/mcse.2021.3059437	900.000000
10	4	2019	Communications of the ACM	39	10.1145/3192335	875.000000
19	1	2021	Computing in Science & Engineering	8	10.1109/mcse.2021.3059263	700.000000
68	4	2021	GSA Bulletin	30	10.1130/b35560.1	650.000000
75	8	2019	International Journal of Remote Sensing	49	10.1080/01431161.2018.1516313	512.500000
28	15	2016	Earth and Space Science	91	10.1002/2015ea000136	506.666667
85	13	2017	JAWRA Journal of the American Water Resources ...	72	10.1111/1752-1688.12474	453.846154
106	5	2017	Journal of Visualized Experiments	25	10.3791/54660	400.000000
25	8	2020	Data Intelligence	40	10.1162/dint_a_00033	400.000000
1	3	2018	AI Magazine	13	10.1609/aimag.v39i3.2816	333.333333
125	39	2017	Scientific Data	167	10.1038/sdata.2017.88	328.205128
5	19	2018	BioScience	77	10.1093/biosci/biy068	305.263158
60	1	2021	Geophysical Journal International	4	10.1093/gji/ggab238	300.000000
17	27	2017	Computers, Environment and Urban Systems	108	10.1016/j.compenvurbsys.2016.10.010	300.000000
20	1	2021	Concurrency and Computation: Practice and Expe...	4	10.1002/cpe.6099	300.000000
122	32	2017	Renewable Energy	127	10.1016/j.renene.2017.02.052	296.875000
100	12	2019	Journal of Proteome Research	44	10.1021/acs.jproteome.8b00761	266.666667

df_citations = pd.read_csv("../outputs/full_nsf_doi_project_summary.tsv", sep='\t')[['doi', 'ams_bib']]

print("""
|Citation| EC Cites | Journal Mean Cites | % diff mean cites|
|---:|:--:|:--:|:--:|""")
for i, r in df_tmp[:10].iterrows():
    doi = df_citations.query(f"doi == '{r['EC Publication DOI']}'").drop_duplicates()['ams_bib'].unique()[0]
    print(f"{doi} | {r['EC Cites']} | {r['Journal Mean Cites']} | {r['% diff mean cites']:.2f}% |")

|Citation| EC Cites | Journal Mean Cites | % diff mean cites|
|---:|:--:|:--:|:--:|
Yang, C., Q. Huang, Z. Li, K. Liu, and F. Hu, 2016: Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10, 13–53, https://doi.org/10.1080/17538947.2016.1239771. | 344 | 17 | 1923.53% |
Morlighem, M., and Coauthors, 2017: BedMachine v3: Complete Bed Topography and Ocean Bathymetry Mapping of Greenland From Multibeam Echo Sounding Combined With Mass Conservation. Geophysical Research Letters, 44, https://doi.org/10.1002/2017gl074954. | 336 | 25 | 1244.00% |
Williams, J. W., and Coauthors, 2018: The Neotoma Paleoecology Database, a multiproxy, international, community-curated data resource. Quaternary Research, 89, 156–177, https://doi.org/10.1017/qua.2017.105. | 120 | 10 | 1100.00% |
Abernathey, R. P., and Coauthors, 2021: Cloud-Native Repositories for Big Scientific Data. Computing in Science &amp; Engineering, 23, 26–35, https://doi.org/10.1109/mcse.2021.3059437. | 10 | 1 | 900.00% |
Gil, Y., and Coauthors, 2018: Intelligent systems for geosciences. Communications of the ACM, 62, 76–84, https://doi.org/10.1145/3192335. | 39 | 4 | 875.00% |
Granger, B. E., and F. Perez, 2021: Jupyter: Thinking and Storytelling With Code and Data. Computing in Science &amp; Engineering, 23, 7–14, https://doi.org/10.1109/mcse.2021.3059263. | 8 | 1 | 700.00% |
Schaen, A. J., and Coauthors, 2020: Interpreting and reporting 40Ar/39Ar geochronologic data. GSA Bulletin, 133, 461–487, https://doi.org/10.1130/b35560.1. | 30 | 4 | 650.00% |
Sun, Z., L. Di, and H. Fang, 2018: Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. International Journal of Remote Sensing, 40, 593–614, https://doi.org/10.1080/01431161.2018.1516313. | 49 | 8 | 512.50% |
Gil, Y., and Coauthors, 2016: Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance. Earth and Space Science, 3, 388–415, https://doi.org/10.1002/2015ea000136. | 91 | 15 | 506.67% |
Maidment, D. R., 2016: Conceptual Framework for the National Flood Interoperability Experiment. JAWRA Journal of the American Water Resources Association, 53, 245–257, https://doi.org/10.1111/1752-1688.12474. | 72 | 13 | 453.85% |

Citation	EC Cites	Journal Mean Cites	% diff mean cites
Yang, C., Q. Huang, Z. Li, K. Liu, and F. Hu, 2016: Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10, 13–53, https://doi.org/10.1080/17538947.2016.1239771.	344	17	1923.53%
Morlighem, M., and Coauthors, 2017: BedMachine v3: Complete Bed Topography and Ocean Bathymetry Mapping of Greenland From Multibeam Echo Sounding Combined With Mass Conservation. Geophysical Research Letters, 44, https://doi.org/10.1002/2017gl074954.	336	25	1244.00%
Williams, J. W., and Coauthors, 2018: The Neotoma Paleoecology Database, a multiproxy, international, community-curated data resource. Quaternary Research, 89, 156–177, https://doi.org/10.1017/qua.2017.105.	120	10	1100.00%
Abernathey, R. P., and Coauthors, 2021: Cloud-Native Repositories for Big Scientific Data. Computing in Science & Engineering, 23, 26–35, https://doi.org/10.1109/mcse.2021.3059437.	10	1	900.00%
Gil, Y., and Coauthors, 2018: Intelligent systems for geosciences. Communications of the ACM, 62, 76–84, https://doi.org/10.1145/3192335.	39	4	875.00%
Granger, B. E., and F. Perez, 2021: Jupyter: Thinking and Storytelling With Code and Data. Computing in Science & Engineering, 23, 7–14, https://doi.org/10.1109/mcse.2021.3059263.	8	1	700.00%
Schaen, A. J., and Coauthors, 2020: Interpreting and reporting 40Ar/39Ar geochronologic data. GSA Bulletin, 133, 461–487, https://doi.org/10.1130/b35560.1.	30	4	650.00%
Sun, Z., L. Di, and H. Fang, 2018: Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. International Journal of Remote Sensing, 40, 593–614, https://doi.org/10.1080/01431161.2018.1516313.	49	8	512.50%
Gil, Y., and Coauthors, 2016: Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance. Earth and Space Science, 3, 388–415, https://doi.org/10.1002/2015ea000136.	91	15	506.67%
Maidment, D. R., 2016: Conceptual Framework for the National Flood Interoperability Experiment. JAWRA Journal of the American Water Resources Association, 53, 245–257, https://doi.org/10.1111/1752-1688.12474.	72	13	453.85%

TAKEAWAYS

journal average citation: 14.96
average citation of EC papers: 24.17 *from WOS metadata citation counts
EC papers beat average 79 times (52.7% of the time)

EarthCube Program Analysis

BASIC JOURNAL ANALYSIS

Contents

BASIC JOURNAL ANALYSIS#

Journal Citation Counts vs Actual Citation Counts#

JOURNAL ANALYSIS#

JOURNALS WITH MORE THAN 1 PUBLICATION#

JOURNALS WITH ONLY 1 PUBLICATION#