You will be presented with the full text of a biomedical research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.

Paper text:
across many areas of science , technological and conceptual advances are resulting in the increasingly rapid generation of large amounts of data . while research has always involved the collection and organization of data , the volume , variety , and velocity of current 
big data production presents new opportunities and challenges in both scale and complexity . at the same time
, there is a broader cultural shift underway from approaches that kept data mostly private with sharing of resultant knowledge in the form of publications to an information - based culture that dynamically engages the scientific community through the active sharing of both data and publications .
big data are not only a new reality for the biomedical scientist , but an imperative that must be understood and used effectively in the quest for new knowledge . needed are new approaches for data management and analysis that allow scientists to better access and extract value from data so as to advance research and discovery.1 
 2 key stakeholders in the coming biomedical big data ecosystem include data providers and users ( eg , biomedical researchers , clinicians , and citizens ) , data scientists , funders , publishers , and libraries .
implementation of such an envisioned biomedical big data ecosystem will depend upon cultural changes and require updated policies related to funding , data sharing , and data citation .
metrics on the efficacy of data sharing and re - use may help inform important decisions at funding agencies and research institutions about the support for and career advancement of biomedical scientists , and could be invaluable in incentivizing necessary cultural changes across the biomedical research enterprise .
the aim of this perspective is to describe how the national institutes of health ( nih ) has started catalyzing such changes through its big data to knowledge ( bd2k ) initiative .
a report to the nih advisory committee to the director from its data and informatics working group ( diwg ) in june 2012 ( http://acd.od.nih.gov/diwg.htm ) recommended the establishment of a broad and inclusive trans - nih program for addressing the opportunities and challenges presented by biomedical big data .
indeed , the spirit of the report suggested that failure of the nih to act at this crucial juncture would represent  institutional malpractice ( as stated by one diwg member ) . to emphasize the need for a coherent and forward - looking response to these opportunities , the position of associate director for data science ( adds ) at nih was created with the goal of placing big data and data science at the highest level of decision - making at the nih .
the first adds , dr philip bourne , has begun his tenure at the nih , and among his responsibilities is oversight of the bd2k initiative .
bd2k has been formulated as a programmatic response to the diwg recommendations and consists of four focused areas : ( 1 ) improving the ability to locate , access , share , and use biomedical big data ; ( 2 ) developing and disseminating data analysis methods and software ; ( 3 ) enhancing training in biomedical big data and data science ; and ( 4 ) establishing centers of excellence in data science .
taking into account the substantial current investment of individual nih institutes and centers in bioinformatics and computational biology within their own spheres of interest , bd2k has first focused on identifying the pressing general but unaddressed needs related to biomedical big data , assessing community expectations , and identifying current policies and practices related to the production , handling , and utilization of biomedical big data .
outreach to the diverse community of stakeholders mentioned above has been at the forefront of these efforts through targeted requests for information ( rfi ) , workshops , consultation with leaders in various fields , as well as extensive discussions across agencies and within the nih about the relationship(s ) between potential bd2k components and other ongoing activities ( http://bd2k.nih.gov/workshops.html ) .
the resulting input strongly ratified the diwg 's recommendations that supporting the development of software and applications for analyzing biomedical big data was only part of the solution .
further support came from the holdren / ostp memo issued in february 2013 ( http://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research ) and directing all agencies of the government to ensure the results of federally funded scientific research are made broadly available .
a first step in fostering the emergence of data science as a discipline relevant to biomedicine is the development of solutions to specific high - need challenges facing the research community .
thus , the first bd2k funding opportunity was for investigator - initiated centers of excellence in data science ( rfa - hg-13 - 009 ) ; this called for proposals to test and validate novel ideas in data science that not only focused on particular challenges but also had the potential for broad impact .
the second launched bd2k area aimed to enhance the training of methodologists and practitioners in data science .
umbrella include computer science , mathematics and statistics , biomedical informatics , biology and medicine , and others , all incorporated as  data science. at the same time , the generation of large amounts of data together with the complex questions being posed , requires interdisciplinary teams to design the studies and perform the subsequent data analyses .
the bd2k training initiatives seek to seed the development of investigators in all parts of the research enterprise who are well - trained in data science .
hand - in - hand with training is the need for cultural changes to assure that the contributions of scientists well - trained in data science are appreciated and rewarded , including the provision of appropriate career paths with commensurate incentives and rewards .
a fundamental question for bd2k is how to enable the identification , access , and citation of ( ie , credit for ) biomedical data .
the diwg proposal for federated data catalogs , as distinct from data repositories , requires descriptions of and pointers to the data .
inherent in data discovery is the need for a sustainable and scalable plan to create and maintain a discovery system that allows researchers to readily find and cite biomedical data .
indeed , sustainability and scalability are two intertwined issues that must be addressed in order for the advances made possible by bd2k to have a lasting effect .
a necessary first step has been recognition of the need to assemble and validate ideas drawn from the broader scientific community in developing a data discovery index ( ddi ) .
the goal of the initial efforts in this area is to define an effective and efficient mechanism(s ) for indexing that will enable the discovery of relevant , existing datasets through the use of metadata and index terms .
the ddi will enable advanced approaches to search , integrate , and facilitate visualization of data .
stakeholders will be encouraged to learn from related efforts in other fields , and conduct short - term pilot studies to explore different ways in which a ddi might be developed and used .
central to development of the ddi will be the ability to link data to associated publications to enhance discovery and facilitate better understanding and interpretation of data and associated analyses .
again , cultural and policy changes will likely be needed to both enhance data - sharing policies and incentivize data sharing . among the difficult challenges
is how best to create greater value from the expanding availability and use of electronic health records ( ehrs ) .
clinical data from ehrs , together with individual health data captured by various personal devices , offer considerable opportunities for advancing clinical and biomedical research .
data from clinical sources could very well provide a cost - effective means to study different health interventions , to conduct large - scale surveillance of disease incidence and progression in real time , to identify patient cohorts for recruitment into clinical trials , and more .
however , unlike most other forms of biomedical research data , clinical data are typically captured outside of traditional research settings and must then be re - purposed for research use .
changes in policies and practices are needed to govern research access to clinical data sources and facilitate their use for evidence - based learning in healthcare . improved approaches to patient consent and
improved quality and quantity of clinical data available for research ( eg , by creating shared libraries of phenotype elements and algorithms and by providing access to key sources of data ) , as well as new methodologies for analyzing clinical data , are all needed for ethical and informed use of these data . while a ddi will undoubtedly help to make data findable and citable , large datasets
can also give value beyond their original purpose when the data are appropriately annotated so that they can be used in a meaningful way .
critical to all of these activities will be the need for formulating , conducting , and maintaining community - based data and metadata standards efforts for reproducibility that will : ( 1 ) be driven by the clearly identified needs of the community ; ( 2 ) include a core group of developers with appropriate expertise ; ( 3 ) engage stakeholders across the community from the outset and in an ongoing way ; and ( 4 ) evolve based on broad input to become increasingly dynamic and responsive to community needs .
addressing the challenges associated with biomedical big data must of necessity engage all parts of the big data ecosystem . while these challenges are complex , they are also addressable .
bd2k is deploying an integrated plan of action that will tackle numerous aspects of the big data challenge , including multiple elements of data science , training , policy , and community behavior . by fostering open communication with the stakeholders
, bd2k will contribute to the broader vision for the future of biomedical big data . while the nih is only one part of the biomedical big data ecosystem
, it can provide leadership for convening stakeholders , providing seed funding , developing metrics for success , implementing a working process , establishing and modifying policies , and supporting basic infrastructure that can catalyze long - term solutions for the biomedical research community . through a working partnership between and among the relevant stakeholders
, useful solutions will emerge and lead to vibrant and sustainable models for capitalizing on biomedical big data and translating data into new knowledge .
biomedical research has and will continue to generate large amounts of data ( termed  big data ) in many formats and at all levels . 
 consequently , there is an increasing need to better understand and mine the data to further knowledge and foster new discovery . 
 the national institutes of health ( nih ) has initiated a big data to knowledge ( bd2k ) initiative to maximize the use of biomedical big data . 
 bd2k seeks to better define how to extract value from the data , both for the individual investigator and the overall research community , create the analytic tools needed to enhance utility of the data , provide the next generation of trained personnel , and develop data science concepts and tools that can be made available to all stakeholders .