Abstract
Data sharing practices within the Earth Sciences vary between disciplines. Each discipline,
indeed each institution, has its own policies and practices to facilitate data sharing. Two major
impediments to data sharing that every discipline must contend with are ensuring data quality
and sharing behaviors among researchers and institutions. This paper will examine data sharing
practices among the Earth Sciences by exploring the data life cycle, in terms of ensuring data
quality, as well as examine motivations and incentives behind data sharing behaviors.
Introduction
Data sharing among the Earth Sciences is invaluable. Sharing research effectively with other
scientists, as well as with the laity, could be argued to be as important as the research itself. If no
one has access to the research or can properly interpret the meaning of findings, then the data
essentially has no meaning. Therefore, data sharing policies and practices are becoming more
common among the scientific community in order to encourage and facilitate the process of data
sharing. This paper will explore the impediments and behaviors of data sharing practices in the
Earth Sciences discipline, and will discuss more specifically programs and institutions designed
to facilitate data sharing in the atmospheric sciences, oceanographic sciences, and astronomy.
Importance of Data Sharing
Before exploring barriers and behaviors of data sharing, it is important to briefly emphasize the
importance of sharing data. Providing accessible, high-quality data encourages “open scientific
inquiry” allowing research to be “validated or refuted” by the scientific community [1]. Data
sharing encourages debate among scientists and prompts further scrutiny of research conclusions
[2]. This further scrutiny then fosters a new level of integrity of the data. Sharing data also
allows other researchers to draw new conclusions from the work and can provide users with a
basis “for new research and new methods of data analysis” [1]. Forming new collaborations
between researchers is often a result of drawing new conclusions between another’s data and
your own [2]. Maintaining large repositories of data offers researchers access to a wide wealth
of information larger than could be generated by an individual or even one institution [1].
Sharing information also prevents the “unnecessary duplication of effort” and thus promotes
greater and faster strides in scientific discovery [1]. This wasted effort is measured not only in
cost, but also in wasted time. Lastly, providing open access to data encourages learning and
discovery among the public and involves the public in the scientific process. By sharing data
with the public, the scientific community has encouraged a movement to citizen science which is
quickly becoming a popular method of sharing data between scientific disciplines. Encouraging
the public to become involved in the data sharing process will become increasingly important as
scientists and the public alike attempt to wade through the age of the data deluge.
Impediments to Data Sharing
There are many impediments to effective data sharing. One of the most basic issues is data
quality. There is no way to discuss data sharing without discussing data quality first. If a
researcher cannot trust the data they are seeking, then sharing the data is useless. The level of
data quality can be measured in many different ways. Following the life cycle of data from
production to management to use/re-use is one way to maintain data quality [3].
In the production phase, two key components are the calibration of the instruments used to
collect the data and the methodology chosen to collect the information [3]. Instruments must be
continually checked and recalibrated in order to maintain a high level of data quality.
Researchers must also choose a most appropriate method of collecting the data, from gathering
general human observations to using a wireless sensing system. The Center for Embedded
Network Sensing realizes the value of collecting trustworthy data and facilitating the reuse of
that data, especially if that data is impossible to reproduce [4]. For example, CENS uses
dynamic sensors which can adjust monitoring conditions in real time [4]. CENS also deploys
scientists into the field with the sensors allowing the scientists to fine tune the instruments;
however, this also brings up other issues with data integrity such as differences in the setup of
the equipment between different teams of scientists. In the past, CENS relied heavily on oral
exchange in terms of equipment usage, calibration, and methodology, but more recently has
discovered the need for consistent documentation to ensure data quality [4], which is the last
component of the production phase. According to CENS scientists, one of their most important
needs is confidence in their measurements. This confidence rests on equipment selection,
equipment calibration, and human reliability [4]. The proper documentation of these
components, whether it be in paper or digital form, is essential to enhancing trust [4]. CENS
applications in the Earth Sciences include their seismic research area. This area implements
network technology to monitor aftershock and volcanic zones [5]. They use a wireless network
with a signal to noise ratio in order to accurately monitor seismic events [5]. However, though
the concept of their Wireless Linked Seismic Network has worked well, there are still problems.
The concept has worked well because the network is actually in Mexico but has been managed
almost completely from the United States. But many problems including hardware failures,
software bugs, weather related failures, and poor design have led to “significant loss of data” [5].
Though most problems were recognized by logging into the network and “probing the sites,” a
field engineer had to be deployed to Mexico to oversee the problems. This illustrates the need
for constant monitoring of instruments and networks in order to safeguard data quality.
The second phase of the data life cycle is data management. This refers to long-term
accessibility of the data [3]. Data is often stored in data archives or repositories specific to
particular disciplines. There are many different archives specific to the area of Earth Sciences
including the National Oceanic and Atmospheric Administration, the British Oceanographic
Data Centre, the National Aeronautics and Space Administration, and the Australian Antarctic
Data Centre. NOAA’s National Climactic Data Center archives its data in the Hierarchical Data
Storage System (HDSS). This system is “the robotic tape assembly used to store large datasets
at NCDC” [6]. The data is then transferred from the tapes onto the public ftp site [6]. The
British Oceanographic Data Centre uses the relational model of database design in order to store
information [7]. This allows tables to have “relationships or links” to other tables [7]. They also
use the National Oceanographic Database to store metadata associated with the datasets. NASA
uses the Distributed Active Archive Centers or DAAC. These centers each serve a particular
discipline in the Earth Sciences to “process, archive, document, and distribute data” from
different satellites and programs [8]. The Australian Antarctic Data Centre (AADC) uses several
databases and a SCAR Feature Catalogue for spatial data [9]. The AADC also has a system
specifically for cataloguing metadata associated with a dataset called the Catalogue of Australian
Antarctic and Sub-Arctic Metadata [9]. Simply looking at these few institutions shows that there
is no one perfect way of storing and sharing data. The system of storing information in a
database for easy retrieval is obviously a commonality, but the specific implementation of
practices and uses varies across institutions.
Another component to data management is “retrievability” [3]. Retrievability refers to the
metadata that accompanies the data as well as the data format. Research data is available in
many different formats including XML, spreadsheet files, database schemas, HTML, Word
documents, PDF format, and many more [10]. The Australian Antarctic Data Centre offers
researchers the option of collecting data in many different formats including TXT, HTML, XML,
MS Excel, CSV, MS Access, JPEG, MPEG, and MP3 just to name a few [9]. The British
Oceanographic Data Centre requires the use of standard formats such as the BODC request
(ASCII) format, Ocean Data View format, a netCDF format, and an AXF format [7]. These
standard formats are described in explicit detail on their website and their researchers required to
format their data by these specific standards. The Global Observing Systems Information Center
(GOSIC) portal, discussed later in greater detail, facilitates data sharing because it returns data
regardless of the format [11]. Again, there is no one specific format for easy data sharing and
each institution maintains their data in a way that works best for them and their researchers. The
UK Data Archive does make recommendations of data formats to use for long-term preservation
of research data [2]. This archive makes recommendations for quantitative tabular data with
extensive metadata, quantitative tabular data with minimal metadata, geospatial data, qualitative
data, digital image data, digital audio data, digital video data, and documentation and scripts. It
is important to understand that the main goal of collecting data in specific formats is to ensure
long-term preservation, accessibility, and usability.
The second issue surrounding retrievability is providing sufficient metadata to support the
understanding and management of data. The three different types of metadata include
descriptive metadata, administrative metadata, and structural metadata [12]. Descriptive
metadata is information regarding the content of the dataset [12]. This type of metadata helps
users to properly interpret the datasets and extrapolate from the datasets or data collections.
Administrative metadata is information which is needed to allow proper management of the data
[12]. This type of metadata is used by those responsible for maintaining the datasets. Structural
metadata describes how different components of associated datasets relate to each other [12].
All these types of metadata are crucial to maintain management of datasets and thus ensure
quality control. GOSIC’s three main systems, the Global Climate Observing System, the Global
Ocean Observing System, and the Global Terrestrial Observing System are required to have
“directory level” and “archive level” metadata associated with their datasets [13]. Directory
level metadata refers to “general descriptive information” needed by a user to identify the dataset
[13]. This includes information about the location of the dataset and contact information.
Archive level metadata refers to the information needed to understand the dataset [13]. At the
Australian Antarctic Data Centre a data record is not complete until all associated metadata has
been submitted [9]. Metadata is essential in enabling data sharing and allowing users to
effectively use data.
Another area to consider when discussing accessibility of research data is data policy issues.
According to a questionnaire posed to a sample of Dutch professors and senior lecturers, open
access to datasets [perhaps after an embargo period] in the field of physical sciences, which also
encompasses Earth Science, is popular [3]. Nonetheless, popularity of open access remains
varied across specific Earth Sciences disciplines. In the field of atmospheric sciences, the
NOAA/National Climactic Data Center Open Access to Physical Climate Data Policy is
essentially full and open data access [14]. According to the policy, all raw data collected from
their many climate observing systems and output from their climate models are all “openly
available in as timely a manner as possible” [14]. Additionally, NOAA makes its derived
datasets available to the public as well as access to climate-related model simulations [14].
NOAA’s National Climate Data Center also operates the Global Observing Systems Information
Center (GOSIC). GOSIC allows people to access international climate related datasets from the
Global Climate Observing System, the Global Ocean Observing System, and the Global
Terrestrial Observing System [11]. The goal of GOSIC is to provide full and open exchange of
data, data products, and metadata for all of these systems at the lowest cost to the user. The
easiest way for users to access information is through the GOSIC portal. This portal does not
contain the datasets, but rather serves as a “single entry point for users” [11]. The portal
maintains information about the datasets and provides users easy access to the data without the
user having to navigate through hundreds of confusing websites trying to find the information
they seek [11].
At NASA, their data sharing policy promotes the “full and open sharing of all data” with those in
academia, the private industry, and the public community [15]. One goal of their policy is to
create a National Information Infrastructure to foster an Environmental Information Economy to
promote a “routine exchange of environmental data” [15]. However, NASA does have the right
to protect data first produced by NASA or by Recipient that contains trade secrets, commercial
or financial information, and other confidential information for a period of two years [15
BODC also promotes the use of their data for the advancement of industry, education, science,
and public knowledge [7]. BODC follows the National Environment Research Council (NERC)
Data Policy in which environmental data will be made available to any person or organization
who wants the data [16]. There are a few restrictions on open access, but those are specifically
explained by the Environmental Information Regulations [16]. Also, in order to protect ongoing
research projects, NERC allows researchers exclusive rights to data they have collected for a
maximum of two years from the end of the data collection period [16]. NERC also requires the
development of a formal data management plan, much like the requirements of the National
Science Foundation [16].
AADC releases submitted data to the public after embargo periods specific to the kind of data
being submitted [9]. For example, ship-sourced observations and measurements are released by
a project’s end data while data on threatened species has an unlimited embargo period [9]. All of
these different institutions illustrate the differences in data sharing policies among the Earth
Sciences disciplines. Even though these institutions all fall under Earth Sciences, they each
maintain specific data policy practices. This is why it is impossible to discuss overall data
sharing practices in the Earth Sciences without discussing different disciplines.
A final thought to accessibility of data, under the management phase of the data life cycle, is
copyright considerations. One proposed solution to copyright issues is the Creative Commons
licenses [3]. Creative Commons promotes “universal access” to research in order to achieve “an
Internet full of open content” [17]. Their mission is to give individuals, institutions, and
companies the ability to keep their copyright but also allow others “certain uses” to their work
[17]. Basically this allows for a “some rights reserved” approach to information sharing rather
than an “all rights reserved” mentality. AADC is one Earth Science institution which uses the
Creative Commons license. Under the Creative Commons Attribution 3.0 License, users are able
to share or to remix the work. The only condition to using the data is that users must attribute
content to the AADC, or, more specifically, to the original creator [17].
The last phase of the data life cycle is use or re-use. In terms of data quality, the use or re-use of
data has to do with the peer review process [3]. This process involves not only the review of
paper publications, but also the quality assurance of datasets. The peer review process is
accomplished in many different ways. One way is to guarantee scientific quality assurance and
formal quality assurance. In scientific quality assurance, the reviewer needs to have significant
knowledge of the topic in order to properly review the information [10]. Since papers are limited
in length, scientific quality assurance on paper publications by a human is feasible [10].
Conversely, since datasets can be huge in scope, reviews on datasets sometimes rely on the help
of computers [10]. The review of the dataset requires the “quality assurance of the data and the
metadata” [10]. On the other hand, formal quality assurance involves reviewing technical
features rather than content. These features include word count, typesetting, and structure [10].
In this aspect of the review process, reviewers do not need significant knowledge of the topic and
so this process is much quicker than scientific quality assurance. An example of this method of
scientific and formal quality assurance is the research project Publication of Environmental Data
[10]. This project involves the use of a software program that examines meteorological data
[10]. This software looks for “outliers and other deviations” based on the parameters set by
researchers and then produces an XML report on which researchers can make annotations
describing the deviations in the data [10]. There is even separate software for monitoring
metadata. This project hopes to ensure data reliability, and thus increased data sharing, by
implementing these review practices.
The importance of the peer review process in data sharing is becoming more important as more
researchers are publishing datasets along with their articles [3]. An evolving practice consists of
the use of special journals for “data publications,” which involve “separate articles describing the
data collection” [3]. An example of this practice is the journal Earth System Science Data. For
this journal, the guidelines of peer review include read the manuscript, check the data quality,
consider the article and dataset, check the presentation quality, and check the publication. The
reviewer then rates the data based on significance, with the sub-criteria of uniqueness,
usefulness, and completeness of the dataset, data quality, and data presentation. The most
important question comes at the end of the process, where the reviewer answers the question “By
reading the article and downloading the dataset would you be able to understand and [re-]use the
dataset in the future?” [18]. This question encapsulates the importance of data quality in
understanding and re-using data and consequently facilitating data sharing.
Another emerging technique of peer review is subsequent peer review. Subsequent peer review,
rather than advance peer review, is a more logical option for peer review of datasets because
reviewing quality of datasets, which can be huge in size, can lead to a significant loss of time
between data gathering and data publication. In the subsequent method of peer review, those
who re-use the data are supposed to write a review about the dataset which is then linked to the
dataset [3]. In the physical sciences discipline, comments on quality by re-users were generally
favored, whereas peer review of the dataset as part of peer review of the publication was seen as
not needed due to concerns about feasibility [3]. Since datasets can become so large, most feel
that requiring peer review of datasets puts an “excessive burden on peer reviewers” which is
viewed as unnecessary [3]. Though peer review of datasets is not widely popular, the overall
importance of peer review in assuring data quality remains a key factor in the data life cycle.
The behavior of researchers and scientists is another impediment to effective data sharing in the
Earth Sciences. A willingness and openness to sharing data is the first step in the process of
effective data sharing. Even though every piece of data and metadata is documented, formatted
correctly, stored, and reviewed, if a researcher is not open to sharing data then having a perfect
data sharing system is useless. Some say that scientists are generally open to the idea of sharing
data and collaborating with their peers, but, when it comes down to actual implementation,
scientists sometimes fall short.
To begin the discussion of sharing behaviors, it is important to first distinguish between the types
of data that researchers are sharing (or not sharing). Raw data is data that has not yet been
processed for use. Raw data includes everything from first hand observational data to the data
that is captured by reading an instrument. Derived data is data that has been “processed or
reduced in some way” and now is meaningful [19]. On a basic level, sharing raw data is difficult
because of its huge scope and unwieldiness [19]. The amount of raw data generated by scientists
is so great that, at times, it cannot practically be shared, and in some cases is not even maintained
for long-term preservation. There are also personal reasons that scientists choose not to share
raw data, such as the desire to build upon the data in future work. Sharing derived data is a more
common practice because it is easier for others to work with and “build on previous findings”
[19]. However, there are concerns that a lack of access to raw data leads to issues about data
integrity. Without raw data, scientists attempting to replicate the results will not know if they
were successful. Replication of results is an integral step in the scientific process and without
the sharing of raw data a system of “checks and balances” in the scientific community disappears
[19]. By sharing both derived and raw data scientists can work to ensure the integrity of the data
as well as increase trust in the quality of the data.
Data sharing behaviors, in terms of publishing datasets, are influenced by a number of different
factors. One of these factors is a lack of time and resources [19]. Many researchers believe that
the cost associated with proper data management is too high. Often times there is also a need for
someone else to expertly curate and manage their data, which just adds to the cost of
management. Many scientists believe that even if there are funds provided to pay for data
management, that these funds should be used to conduct more research and not to manage and
preserve data [19]. This mentality is similar to many researchers’ opinions on the amount of
time needed to manage data. There is a good deal of time needed to effectively manage data and
researchers feel that this time does not justify the benefits of managing their data. Also, if
researchers do publish their datasets, there is a concern about the amount of time spent dealing
with requests for additional information [19]. Processing requests for information and providing
explanations of their datasets requires valuable time that researchers believe could be better used
in actually conducting and analyzing research. Researchers fear that valuable time will be
wasted on gathering and transmitting explanations such as what tools were used or associated
metadata [19].
Another constraint in publishing datasets is a lack of experience or skill in managing data,
“especially making it accessible and usable” [19]. Many researchers find the data management
process to be an “unfamiliar and daunting prospect” [19]. However, in the field of Earth
Science, many data centres and institutions do provide assistance in managing data. NERC data
centres provide “support and guidance in data management to those funded by NERC” [16].
AADC ensures that data is easily accessible and “managed for the long-term” [9]. BODC has an
international reputation for its proficiency in handling data. BODC’s main objective is to
“ensure good data management practices” and facilitating data sharing [7]. Nevertheless, though
data centres may have good policies and practices in place, ultimately they depend on
“willingness to share” [20]. Even with assistance from some data centers and institutions, some
scientists still choose not to seek help and thus struggle with proper data management [19]. In
addition to a lack of skill surrounding data management, scientists also at times have a lack of
knowledge as to where to archive their data [19].
Competitive factors and fear of being “scooped” are major concerns for most researchers [19].
Some scientists fear that sharing their data too soon is a significant risk and that anyone could
then take credit for their research and work. Researchers sometimes wish to have exclusive use
of their data collections and control who has access to them in order to reduce the risk of being
scooped [19]. As previously discussed, many data centers and institutions have embargo periods
on data use, such as NASA and the AADC, but many researchers still do not want to share their
data. Another fear is that of exploitation and that perhaps others might misrepresent their data or
that “unwarranted conclusions may be drawn” [19].
An additional consideration in sharing datasets is the uncertainty that they are useful [19]. Some
find it hard to believe that “anyone will want access to their datasets” [19]. This is not
particularly applicable to most datasets produced by the Earth Sciences, but some researchers do
feel that model-run data or datasets produced from small scale projects are not necessarily in
high demand [19]. Though this uncertainty is not a main concern in the Earth Sciences
discipline, it is still a consideration for researchers when deciding to share data.
Lastly, data sharing behaviors are influenced by a lack of incentives or rewards [19]. As David
Carlson, former director of the International Polar Year International Programme Office, puts it
“Earth scientists need better incentives, rewards and mechanisms to achieve free and open data
exchange” [20]. He believes that though issues like data quality, data management, and
technical impediments do hinder data sharing, the real problem is behavior [20]. Carlson
believes that practical solutions such as the development of a ‘Polar Information Commons,’ data
centers willingness to share, and the establishment of journals such as the Earth System Science
Data journal, mentioned previously, are necessary steps in changing the behavior of scientists
involved in the International Polar Year Programme. However, many researchers still believe
that there is not much benefit to them for spending the time and effort required to effectively
share data or datasets.
The Research Information Network (RIN), in association with the Joint Information Systems
Committee (JISC) and the Natural Environment Research Council, conducted a study to analyze
the sharing behaviors of different scientific disciplines. Their study discovered underlying
motivations to publish datasets as well as benefits and incentives to sharing datasets. One reason
scientists fail to share data is that there are “no career-related rewards for sharing or publishing
datasets” [21]. If sharing is not recognized in terms of increased funding, then scientists feel
little reason to share. Also, according to this study, data publishing is a distant second to
publishing papers [21]. Of course there are also positive motivations to sharing datasets as well.
Altruistic motivations and acting for the good of the progression of science is a positive
motivation [21]. The study found that researchers who commonly share data are much more
likely to have the behavior reciprocated and also feel freer to ask peers for use of their data [21].
Another motivation is “greater visibility” for the researcher or for the institution or group they
represent [21]. The more published datasets that a researcher shares the more recognized and
visible they become, which could lead to more opportunities for collaborations among
researchers and institutions [21]. These collaborations could be between those within the same
discipline or they could be interdisciplinary collaborations. Sharing data can also lead to
opportunities to co-author papers with other researchers [21]. The study also found that
encouragement among peers is a motivation to sharing datasets [21]. Thus simple
encouragement can sometimes foster data sharing among different disciplines. Finally, a
personal interest in data sharing and data preservation is often times an incentive to effective data
sharing [21].
According to this same study, researchers elaborated on specific incentives that would
“encourage them to devote more attention to publishing or sharing their data” [21]. One
incentive is to give evidence, through case studies, that there are benefits to publishing datasets
[21]. Researchers want there to be some value in taking the time manage and preserve their
datasets for future use. Another incentive researchers cited is more defined rewards involving
“career progression” [21]. Researchers believe this can be accomplished in many ways including
“closing the gap” between the rewards gained for publishing a paper rather than publishing a
dataset and “taking account” of past data sharing behaviors when determining grant funding. A
final incentive that researchers cited to foster sharing data is a “standard, workable” method of
citing their datasets [21].
A brief summary of data sharing in the fields of Astronomy and Climate Science in the
United Kingdom
According to the same report commissioned by the Research Information Network, there are
certain conclusions that can be drawn about data sharing practices based on the results of their
interviews with researchers in different disciplines. In the field of astronomy, the cost of
curating and preserving data is very high [21]. The cost of storage space is substantial, though
according to the report the cost is decreasing, as well as the cost to train those who will care for
the data long-term [21]. Another issue is accessibility to older data because the software
required to support this is “no longer adequately supported” [21]. In terms of motivations and
constraints in publishing data, astronomers will almost always provide data for requested
datasets as long as the embargo period is past [21]. However, astronomers are much more
reluctant to share information that has not yet been published [21]. According to the report, if
there are no mandatory policies for data sharing, the motivation to share is likely “career reward”
from publishing journal articles [21]. In general, the culture of data sharing in the field of
astronomy is high with few infrastructure-related barriers to publishing data and the tendency to
publish datasets, with metadata and appropriate documentation, is high [21].
In the climate sciences discipline, opinions about data preservation seem to depend on the type of
data being preserved. Many believe that the lifespan for model run data is five years since it
becomes obsolete quickly, while raw data, especially observations, “should be curated for the
long-term” [21]. The value of these two types of data differs as well, in that many believe raw
model run data is only valuable to the creator while raw observational data is valuable to all [21].
Of course, value is added to the raw model run data after it is processed [21]. Researchers in the
climate sciences, tend to “respond positively” to requests for access of datasets, though low
demand for model run data discourages researchers from properly managing their data to make it
understandable to all [21].
Conclusion
As Carlson stated in his article A Lesson in Data Sharing, “A perfect data sharing system is
science’s ‘unobtainium’” [20].
As Albert Einstein said, “The restriction of knowledge to an elite group destroys the spirit of
society and leads to its intellectual impoverishment.” In the case of data sharing in the Earth
Sciences, this is most assuredly true.
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