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Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine

Ana María Sánchez-López
Purificación Catalina
Fernando Franco
Sonia Panadero-Fajardo
Juan David Rejón
María Concepción Romero-Sánchez
Jose Manuel Puerta-Puerta
1,3 and
Rocío Aguilar-Quesada
Andalusian Public Health System Biobank, Coordinating Node, 18016 Granada, Spain
Instituto de Investigación Biosanitaria Ibs.GRANADA, 18012 Granada, Spain
Unidad de Gestión Clínica Hematología y Hemoterapia, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain
Author to whom correspondence should be addressed.
J. Pers. Med. 2024, 14(7), 668;
Submission received: 25 April 2024 / Revised: 5 June 2024 / Accepted: 18 June 2024 / Published: 21 June 2024
(This article belongs to the Special Issue Biobank and Biorepository in Personalized Medicine)


Biobanks are infrastructures essential for research involving multi-disciplinary teams and an increasing number of stakeholders. In the field of personalized medicine, biobanks play a key role through the provision of well-characterized and annotated samples protecting at the same time the right of donors. The Andalusian Public Health System Biobank (SSPA Biobank) has implemented a global information management system made up of different modules that allow for the recording, traceability and monitoring of all the information associated with the biobank operations. The data model, designed in a standardized and normalized way according to international initiatives on data harmonization, integrates the information necessary to guarantee the quality of results from research, benefiting researchers, clinicians and donors.

1. Introduction

A widely accepted definition of biobank includes the management of samples (biological materials) but also their related information and data [1,2], playing a crucial role in biomedical research by providing biological samples and associated data for scientific purposes. Biobanks are professionalized infrastructures that require increasingly complex governance models involving many stakeholders, including donors, researchers, clinicians, funders and industry, playing a central role in sustainability [3]. In addition, the governance of biobanks must be adapted to the needs of and trends in research, with the highlighted role of next-generation biobanking for personalized medicine [2]. Indeed, key areas of expansion of precision medicine have been identified [4], of which biobanks will be a part.
In this context, one of the cutting-edge factors impacting biobank activity is digitalization and virtualization, with biobanks of images for precision medicine being a clear example [5]. Virtualization has innumerable advantages for biobanks, with digitized information associated with or derived from the analysis of samples from different platforms for biomedical research. It adds value to the samples available upon becoming infinite and perpetual resources and that are still available even after the sample is finished. Digitalization and virtualization allow for better and more sustainable preservation of the sample that becomes an archived digital image; it does not deteriorate and it does not lose integrity due to the storage. However, virtualization makes it necessary to have a biobank information management system (BIMS), a robust tool used to record, trace and monitor all the information associated with the samples and biobank operations, some of whose functioning has been previously reported [6].
The diversity in nature and the format of collected data involves significant challenges for the biobanks. Through the effective implementation of standards in the data acquisition, coding and management process, the quality and thereby confidence in data integrity is improved. Furthermore, standardization enables interoperability between different systems, facilitating international collaboration by integrating data and comparing them among biobanks from different countries and regions. The following are the most commonly used general and specifically developed standards in biobanking data coding and harmonization:
ICD (International Classification of Diseases) [7]: A classification system used to classify and code diseases, disorders, injuries and causes of death, as well as other health conditions of donors.
SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) [8,9]: A medical coding system based on a hierarchical system of concepts and semantic relationships used to encode data on types of biological samples, diseases, laboratory procedures, medical treatments and other relevant clinical entities.
OMOP (Observational Medical Outcomes Partnership) [10,11,12,13]: A data model designed to standardize and analyse clinical data in observational studies, providing a standard framework for data structure and nomenclature. It allows us to store medical data with standardized terminologies. Furthermore, it includes several applications that enable the analysis of patient data for different research purposes.
BRISQ (Biospecimen Reporting for Improved Study Quality) [14]: A set of recommendations structured into three levels that provide detailed guidelines on how to document and present information about biological samples in biobanks.
MIABIS (Minimum Information About Biobank Data Sharing) [15,16,17]: A set of standards defining the minimum information required to describe a biobank and its samples.
SPREC (Sample PREanalytical Code) [18,19,20,21]: A seven-element code designed for providing details about preanalytical variables including the collection, processing and storage of fluid and solid biological samples.
Additionally, the application of FAIR principles (Findable, Accessible, Interoperable, Reusable) during the standardization process is widely recommended by international organizations and institutions to facilitate the use of data by the scientific community in research projects and promote interoperability [22].
Considering all the above, the SSPA Biobank has implemented a comprehensive BIMS made up of different modules that allow for the recording, traceability and monitoring of all the information associated with the biobank operations, including samples, donors and requests. The BIMS data model, described in this work, has been designed in a standardized and normalized way according to previous international initiatives on data coding, harmonization and access and considering the recommendations in data collection and their management provided by different initiatives and organizations [23,24]. Questionnaires and additional references were also used when necessary for the establishment of specific datasets [25,26].

2. Design of the Data Model and Their Integration in the Information Management System: Relationship of the Information and Exploitation

The SSPA Biobank’s BIMS, called nSIBAI, has been co-developed by the Andalusian Public Health System and the company Biosoft Innovation S.L. The technology used for its development, the Mongo DB data system, gives it great capacity and versatility to incorporate new functionalities and adapt the data model through a relatively simple configuration while maintaining the necessary security. In addition, it allows integrations with other software to facilitate access to additional information and speed up all the tasks performed [27]. Currently, nSIBAI is working in more than 20 nodes of the SSPA Biobank, operating as a virtual biobank [28,29].
nSIBAI is organized in different modules that are connected between them. The principal modules which integrate the BIMS are as follows:
  • Donor management: donors with sample donations to the Biobank, and potential donors registered in the Andalusian Registry of Donors for Biomedical Research (REDMI) [27].
  • Sample/donation management: this includes three main areas: (1) collection of samples and data (donations), (2) sample processing data and (3) stored sample data.
  • Request/project management: this module is organized in four areas:
    Legal, ethical and administrative management data related to the requests.
    Follow-up of sample and data acquisition.
    Deliveries of samples and data to the researchers/users.
    Return of research results.
The interrelationship of these modules allows the SSPA Biobank to manage information in order to integrate all the activities and offer a quality service for research. Moreover, the BIMS has supporting and configuration modules (Figure 1).
To record the information of each module, the BIMS has fields directly associated with the items or questionnaires. Questionnaires are organized in at least four groups: (1) donors, (2) donations, (3) samples and (4) processes. All of them are designed by specific staff from the SSPA Biobank from a pool of questions, and they are capable of creating new questions and questionnaires and modifying them in order to adapt them to new needs of the Biobank related to biomedical research (Figure 2). To guarantee that critical data are available and consistent, a procedure for the quality control of data has been developed.
Relevant fields and questions corresponding to the modules of nSIBAI’s data model are shown as tables, classified by but not strictly related to the following:
Donor-associated data (Table 1).
Donation-associated data (Table 2).
Data related to the protection of the rights of donors (Table 3).
Sample and process-associated data (Table 4).
The tables show the information collected (data group and data description), the type of data (text, number, list or date), the way of recording data (field, questions included in a questionnaire or automatically filled in by the BIMS) and if these data or related information are part of BRISQ, MIABIS, OMOP or SPREC standards as references for data harmonization (√). On the other hand, when this information is used to access samples through the catalogue of the SSPA Biobank, it is indicated.
Table 1. Donor-associated data.
Table 1. Donor-associated data.
Data GroupData DescriptionType of DataRecordingBRISQMIABISOMOPCatalogue
Sample donor IDNumericAutomatic code
Type and Number IDList, NumberFields
First and Last NameTextFields
Clinical number IDNumberFields
Demographic dataSexListField
Birth dateDateField
Country of birthListField
City and state of birthListField
City and state of residence (n times)ListField
Contact informationE-mailTextField
Landline or Mobile PhoneNumberFields
Health data (information collected through donor)Disease statusTextField
Clinical or pathology diagnosisTextField
Diagnosis date (chronic diseases)TextField
Clinical characteristicsTextField
Epidemiological characteristicsTextField
Family’s medical historyTextField
Kinship relationsFamily tiesListField
Recruitment informationName of divulgation eventListField
Member of patient associationsListField
Table 2. Donation-associated data.
Table 2. Donation-associated data.
Data GroupData DescriptionType of DataRecordingBRISQMIABISOMOPCatalogue
Donation identificationDonation IDAlphanumericAutomatic code
Collection (event) date and timeDate/TimeField
Source codeTextField
Visit conceptListField
Visit concept valueNumber or listField
Age at eventAuto calculatedAutomatic
Care source unitListField
Clinical data: pathological or controlDiagnosis CIE-10ListQuestionnaire
Health control groupListQuestionnaire
Diagnosis SNOMED-CTListQuestionnaire
Diagnosis SNOMED IITextQuestionnaire
Non-codified diagnosisTextQuestionnaire
Disease statusListQuestionnaire
Debut dateDateQuestionnaire
Diagnosis dateDateQuestionnaire
Clinical data: treatment and follow-upTreatmentListQuestionnaire
Treatment typeListQuestionnaire
Treatment dateDateQuestionnaire
Treatment responseTextQuestionnaire
Disease-free survivabilityNumberQuestionnaire
ECOG scaleListQuestionnaire
Health perceptionGeneral health perceptionTextQuestionnaire
Health perception compared to othersTextQuestionnaire
Health perception compared to last yearTextQuestionnaire
Health-related limitationsTextQuestionnaire
Lifestyle and consumption habitsDietary habitsTextQuestionnaire
Exercise frequencyTextQuestionnaire
Regularity of alcohol-drinkingTextQuestionnaire
Tobacco consumptionListQuestionnaire
Another drugs consumptionListQuestionnaire
Table 3. Data related to the protection of the rights of donors.
Table 3. Data related to the protection of the rights of donors.
Data GroupData DescriptionType of DataRecordingMIABIS
Informed consentSigned consent dateDateField
Consent fileArchiveField
Legal representative IDNumberField
Legal representative information (name and surname)TextField
Professional ID involved in the information process NumberField
Professional identification (name and surname)TextField
Collection methodListField
Detail of collection method TextField
Identification of sample (codified or anonymized) ListField
Consent to contact laterListField
Ways to contactListField
Detail of contact (phone, email…)TextField
Consent to receive genetic or other health relevant informationListField
Authorized research areas, education or quality controlListField
Use restrictionsTextField
RevocationRevocation dateDateField
Revocation type (partial or total)ListField
Revocation fileArchiveField
Other considerationsTextField
Table 4. Sample-associated data.
Table 4. Sample-associated data.
Data GroupData DescriptionType of DataRecordingBRISQMIABISOMOPSPRECCatalogue
Sample identificationSample IDAlphanumericAutomatic code
Source codeTextField
Applied process dataProcess applied IDAlphanumericAutomatic code
Process applied nameList Field
Start date and timeDate/timeField
End date and timeDate/timeField
Pre-analytical dataType of sampleList Field
Sample characteristicsList Field
Type of cellular lineList Field
Anatomical siteList Field
Quantity of sample (volume or size)NumberField
ContainerList Field
AdditiveList Field
Collection date and timeDate/timeField
Type of collection/collection mechanismList Field
Reception temperatureNumberField
Warm ischemia timeListQuestionnaire
Cold ischemia timeListQuestionnaire
Cold ischemia temperatureListQuestionnaire
Fixation timeNumberQuestionnaire
Reception date and timeDate/TimeField
Pre-centrifugation delayListQuestionnaire
Pre-centrifugation temperatureListQuestionnaire
Centrifugation speedNumberQuestionnaire
Centrifugation timeNumberQuestionnaire
Centrifugation temperature NumberQuestionnaire
Centrifugation: strokeListQuestionnaire
Post-centrifugation delayListNumber
Post-centrifugation temperatureListQuestionnaire
Freezing methodListQuestionnaire
Freezing temperatureListQuestionnaire
Long-term storage temperatureListQuestionnaire
Long-term storage containerListQuestionnaire
Start date and time of storageDate/TimeField
Quality/Analytical dataThawing methodListQuestionnaire
Cellular viability (%) and others relatedNumberQuestionnaire
Medium of cultureListQuestionnaire
Method of acid nucleic extractionListQuestionnaire
Method of quantificationListQuestionnaire
Ct value (specific for each PCR: flu, SARS-CoV, VPH…)NumberQuestionnaire
Concentration and others relatedNumberQuestionnaire
Immunohistochemical study (Ab and result)ListQuestionnaires
Histochemical study (staining and result)ListQuestionnaires
Value of STRs NumberQuestionnaire
Chromosome and genetic identification methodListQuestionnaires
Chromosome formula and others relatedText/numberQuestionnaires
Image of karyotypeArchiveQuestionnaire
Biochemistry parameters (cholesterol, LDL, Protein C, Vitamin D, Glucose…)Number Questionnaires
Screening (positive/negative) microbiological agents
(Herpes, SARS-CoV-2, Citomegalovirus, …)
Haematological parameters (lymphocytes, erythrocytes, …)Number Questionnaires
Histological evaluation List/TextQuestionnaires
Histological gradeListQuestionnaire
Technical reportArchiveQuestionnaire

3. Provision of Samples and Associated Information and Return of Research Results

Researchers can access SSPA Biobank’s collections of samples and associated data through a well-defined procedure. Briefly, the requests are made through a form in which researchers describe the needs of samples and/or associated data for a project previously approved by an ethical committee. The Biobank evaluates whether the samples are already available in the stock of the SSPA Biobank and whether the associated data are recorded, or if a prospective collection of samples or an update of data are also necessary. Prior to providing the samples and data required by the project, the Biobank will require approval from its external ethics and scientific committees, which includes the number and type of samples and associated data being fit for purpose, in compliance with specific Spanish national laws (Law 14/2007 on Biomedical research and Royal Decree 1716/2011 on Biobanks and Human Biological Samples). Both committees provide an additional guarantee of equitable access to the biospecimens and data by the researchers according to the rights of donors. Finally, a Material Transfer Agreement (MTA) is signed between the SSPA Biobank and the researcher before sending samples. Data associated with samples are provided to researchers by email as a confidential file attached in compliance with EU General Data Protection Regulation 2016/679 and the specific Spanish national Organic Law 3/2018 on the Protection of Personal Data and Guarantee of Digital Rights. This process records and monitors using the SSPA Biobank BIMS, and it will be possible to request the biospecimens using this tool in the near future.
MTA includes, among other things, the obligation of the researcher to inform the Biobank of the genetic results relevant for the health of donors and incidental findings, as well as any scientific publication or technical document, communication and intellectual or industrial property document performed with the samples and associated data provided. The information related to research results is recorded through a dataset composed of different fields, some of them general for all research results and others specific for each type (Table 5). The importance of the recognition in communications of the biobanks as support infrastructures has been highlighted by the community, and a specific initiative, the Bioresource Research Impact Factor/Framework (BRIF), was even developed to quantitatively evaluate the impact of the use of bioresources in research [30,31]. Consequently, CoBRA (Citation of BioResources in journal Articles) was considered to identify the research results mentioning the SSPA Biobank to record in the BIMS associated with the corresponding provision [32,33].
As an added value, the interaction between the biobank and the researchers can be based on a win-win relationship. MTA could be extended based on a specific governance model associating the raw data (e.g., genomics, metabolomics, and images) from analysis developed by the researcher to the samples and contributing to the virtual biobank. These types of data could be included in the BIMS through different strategies: storing the data in the BIMS or linking with the original source of data for access through specific identifiers.

4. Conclusions

The BIMS data model of the SSPA Biobank has been designed and adapted in compliance with the available standards, integrating the information necessary to guarantee the Biobank operations. The benefits for researchers are related to the reproducibility of research thanks to the better annotation of specimens and the availability of an increasing amount of associated data useful for research as well as the access to samples through the virtual catalogue. Clinicians collecting samples will also have all this information available, including incidental findings, making it possible to achieve better diagnostics and treatments of their patients. The benefits for donors derive from advances in health and from an exhaustive characterization of their samples allowing for precision medicine. However, in the frame of the Quality Management System of the SSPA Biobank, the BIMS data model will continue to improve by adding or supporting new attributes reported by the international community.

Author Contributions

Conceptualization, R.A.-Q. and A.M.S.-L.; data curation, A.M.S.-L. and F.F.; writing—original draft preparation, R.A.-Q., P.C., F.F., S.P.-F., J.D.R., M.C.R.-S. and A.M.S.-L.; writing—review and editing, R.A.-Q. and A.M.S.-L.; project administration, A.M.S.-L., R.A.-Q. and J.M.P.-P. All authors have read and agreed to the published version of the manuscript.



Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data produced in this study are shown in this article.


We would like to thank the support provided by staff from Fundación Pública Andaluza Progreso y Salud M.P. and Biosoft Innovation S.L. for the technical maintenance of nSIBAI.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the identification, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.


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Figure 1. Interaction of main modules of nSIBAI.
Figure 1. Interaction of main modules of nSIBAI.
Jpm 14 00668 g001
Figure 2. N questionnaires of nSIBAI are created from a pool of questions in response to the information to record.
Figure 2. N questionnaires of nSIBAI are created from a pool of questions in response to the information to record.
Jpm 14 00668 g002
Table 5. Data model for publications or technical documents, communications and intellectual or industrial property documents as research results performed with samples and associated information provided by the Biobank.
Table 5. Data model for publications or technical documents, communications and intellectual or industrial property documents as research results performed with samples and associated information provided by the Biobank.
Data GroupData DescriptionType of Data
Common dataSummary of project results (divulgation version)Text
Summary of project resultsArchive
Classification of research result (congress, publications or industrial/intellectual property)List
Title of resultText
Publications or technical scientific documents (articles, book, guides, thesis…)AuthorsText
Mention of BiobankList
Publication typeList
Corresponding authorText
Name of journalList
Indexing (Impact Factor, Quartil, Decil)Number
Publication yearNumber
Publication dateDate
Publication pagination (volume/number/pages)Number
Publication registration ID (PMID/ISSN/DOI/ISBN)Number
Link to publicationSpecial Text
Research resultArchive
Congress/conference/symposium nameText
Magazine publicationList
Place of celebration (city and country)Text
Date of celebrationDate
Organizer entityList
Even typeList
Type of participationList
Geographical scopeList
Research resultArchive
Industrial and intellectual propertyType of industrial propertyList
Rights holder entityText
Application dateDate
Application numberText
Country of registrationText
Registration dateDate
License concession dateDate
Protection modeList
Patent IDText
PCT PatentText
Spanish patentNumber
Country of propertyList
Exploitation statusList
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Sánchez-López, A.M.; Catalina, P.; Franco, F.; Panadero-Fajardo, S.; Rejón, J.D.; Romero-Sánchez, M.C.; Puerta-Puerta, J.M.; Aguilar-Quesada, R. Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine. J. Pers. Med. 2024, 14, 668.

AMA Style

Sánchez-López AM, Catalina P, Franco F, Panadero-Fajardo S, Rejón JD, Romero-Sánchez MC, Puerta-Puerta JM, Aguilar-Quesada R. Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine. Journal of Personalized Medicine. 2024; 14(7):668.

Chicago/Turabian Style

Sánchez-López, Ana María, Purificación Catalina, Fernando Franco, Sonia Panadero-Fajardo, Juan David Rejón, María Concepción Romero-Sánchez, Jose Manuel Puerta-Puerta, and Rocío Aguilar-Quesada. 2024. "Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine" Journal of Personalized Medicine 14, no. 7: 668.

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