Types Of Metadata Research Articles

Multimodal masked siamese network improves chest X-ray representation learning

Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. Although such approaches deliver promising results, they do not take advantage of the associated patient or scan information collected within Electronic Health Records (EHR). This study aims to develop a multimodal pretraining approach for chest radiographs that considers EHR data incorporation as an additional modality that during training. We propose to incorporate EHR data during self-supervised pretraining with a Masked Siamese Network (MSN) to enhance the quality of chest radiograph representations. We investigate three types of EHR data, including demographic, scan metadata, and inpatient stay information. We evaluate the multimodal MSN on three publicly available chest X-ray datasets, MIMIC-CXR, CheXpert, and NIH-14, using two vision transformer (ViT) backbones, specifically ViT-Tiny and ViT-Small. In assessing the quality of the representations through linear evaluation, our proposed method demonstrates significant improvement compared to vanilla MSN and state-of-the-art self-supervised learning baselines. In particular, our proposed method achieves an improvement of of 2% in the Area Under the Receiver Operating Characteristic Curve (AUROC) compared to vanilla MSN and 5% to 8% compared to other baselines, including uni-modal ones. Furthermore, our findings reveal that demographic features provide the most significant performance improvement. Our work highlights the potential of EHR-enhanced self-supervised pretraining for medical imaging and opens opportunities for future research to address limitations in existing representation learning methods for other medical imaging modalities, such as neuro-, ophthalmic, and sonar imaging.

From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization.

Leaving the context of visualizations invisible can have negative impacts on understanding and transparency. While common wisdom suggests that recontextualizing visualizations with metadata (e.g., disclosing the data source or instructions for decoding the visualizations' encoding) may counter these effects, the impact remains largely unknown. To fill this gap, we conducted two experiments. In Experiment 1, we explored how chart type, topic, and user goal impacted which categories of metadata participants deemed most relevant. We presented 64 participants with four real-world visualizations. For each visualization, participants were given four goals and selected the type of metadata they most wanted from a set of 18 types. Our results indicated that participants were most interested in metadata which explained the visualization's encoding for goals related to understanding and metadata about the source of the data for assessing trustworthiness. In Experiment 2, we explored how these two types of metadata impact transparency, trustworthiness and persuasiveness, information relevance, and understanding. We asked 144 participants to explain the main message of two pairs of visualizations (one with metadata and one without); rate them on scales of transparency and relevance; and then predict the likelihood that they were selected for a presentation to policymakers. Our results suggested that visualizations with metadata were perceived as more thorough than those without metadata, but similarly relevant, accurate, clear, and complete. Additionally, we found that metadata did not impact the accuracy of the information extracted from visualizations, but may have influenced which information participants remembered as important or interesting.

Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning.

In order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The clinical annotation of datasets is usually performed at the expense of the data volume, which results in poorer algorithm performance. This study aimed to evaluate the application of an MLA for the automated classification of physiological optic discs (PODs), glaucomatous optic discs (GODs), and glaucoma-suspected optic discs (GSODs). Annotation of the data to the three groups was based on the diagnosis made in clinical practice by a glaucoma specialist. Color fundus photographs and 14 types of metadata (including visual field testing, retinal nerve fiber layer thickness, and cup-disc ratio) of 1168 eyes from 584 patients (POD = 321, GOD = 336, GSOD = 310) were used for the study. Machine learning (ML) was performed in the first step with the color fundus photographs only and in the second step with the images and metadata. Sensitivity, specificity, and accuracy of the classification of GSOD vs. GOD and POD vs. GOD were evaluated. Classification of GOD vs. GSOD and GOD vs. POD performed in the first step had AUCs of 0.84 and 0.88, respectively. By combining the images and metadata, the AUCs increased to 0.92 and 0.99, respectively. By combining images and metadata, excellent performance of the MLA can be achieved despite having only a small amount of data, thus supporting ophthalmologists with glaucoma diagnosis.

BASECOL2023 scientific content

Context. The global context of making numerous data produced by researchers available requires collecting and organising the data, assigning meaningful metadata, and presenting the data in a meaningful and homogeneous way. The BASECOL database, which collects inelastic rate coefficients for application to the interstellar medium and to circumstellar and cometary atmospheres, meets those requirements. Aims. We aim to present the scientific content of the BASECOL2023 edition. Methods. While the previous versions relied on finding rate coefficients in the literature, the current version is populated with published results sent by the producers of data. The paper presents the database, the type of data that can be found, the type of metadata that are used, and the Virtual Atomic and Molecular Data Centre (VAMDC) standards that are used for the metadata. Finally, we present the different datasets species by species. Results. As the BASECOL database, interconnected with the VAMDC e-infrastructure, uses the VAMDC standards, the collisional data can be extracted with tools using VAMDC standards and can be associated with spectroscopic data extracted from other VAMDC connected databases such as the Cologne database for molecular spectroscopy (CDMS), the jet propulsion laboratory molecular spectroscopy database (JPL), and the high-resolution transmission molecular absorption database (HITRAN).

The Path of Least Resistance: Optimizing Metadata Practices through User Assessment

As part of a multi-faceted research project examining user engagement with various types of descriptive metadata to improve metadata services, Utah State University Libraries Cataloging and Metadata Services unit investigated user search behavior in library catalog (MARC) records, Encoded Archival Description (EAD) finding aids, and most recently in digital collection metadata. The authors used web log analysis to determine how users interacted with CONTENTdm, categories of search terms used, and where search terms were found in a record. Key findings included that navigation through CONTENTdm using clickable queries (links) is a prominent user search pattern, dates are an important faceting tool and date ranges as a clickable link are heavily used, users demonstrated a more engaged research pattern when searching with geographic terms, and subject and transcription metadata fields match user search terms at a significant rate and are likely to be high drivers of search results.

Peculiarities of Europeana Metadata as a Set of Information for Describing Digital Cultural Heritage

The relevance of the research. European integration is the main and unchanging foreign policy priority of Ukraine. Further development and deepening of relations between Ukraine and the EU are the main direction of its development. A new stage in the development of EU-Ukraine treaty relations, which began with Ukraine’s accession to the EU candidate status in June 2022, requires further raising awareness of Ukraine’s European integration among all members of society. Libraries can contribute to the formation of objective motivations among the population by understanding the practical benefits of cooperation with the EU. One of the ways of such integration for Ukrainian libraries is to become part of the European library network.
 The purpose of research is to identify the features of Europeana metadata for further digitization of library collections in Ukraine in order to preserve cultural heritage.
 The methodology. To achieve this goal, theoretical and general scientific research methods, such as analysis, synthesis, induction, and deduction, were used. The data visualization method and the method of generalization were used to present the Europeana data models.
 The results. The article defines the types of the Europeana operational model, provides a general overview of the classes defined in the Europeana data model and the process of interaction between Europeana data and wikidata.
 The scientific topicality. The article summarizes previous, primarily foreign, research on the peculiarities of the development of the Europeana funds. The article presents the data models of the Europeana that function to fill the web portal.
 The practical significance. The results of the research are presented in the form of Europeana data models and the definition of Europeana metadata types (descriptive, structural, administrative, markup language).
 The conclusion. The current operating model of Europeana uses the Open Archives Initiative’s Metadata Collection Protocol (OAI-PMH) and the Europeana Data Model (EDM) to import data through Metis, Europeana’s data collection and aggregation service. However, OAI-PMH is an outdated technology that is not web-oriented, resulting in high maintenance costs, especially for smaller institutions. Metadata can be divided into several categories, or types, for different data management purposes. Traditional library cataloging focuses, for example, on the identification and description of resources, but there are obviously other types of metadata that carry valuable information (structural, administrative, markup language).

A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study.

In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.

Metadata Standard for Continuous Preservation, Discovery, and Reuse of Research Data in Repositories by Higher Education Institutions: A Systematic Review

This systematic review synthesised existing research papers that explore the available metadata standards to enable researchers to preserve, discover, and reuse research data in repositories. This review provides a broad overview of certain aspects that must be taken into consideration when creating and assessing metadata standards to enhance research data preservation discoverability and reusability strategies. Research papers on metadata standards, research data preservation, discovery and reuse, and repositories published between January 2003 and April 2023 were reviewed from a total of five databases. The review retrieved 1597 papers, and 13 papers were selected in this review. We revealed 13 research articles that explained the creation and application of metadata standards to enhance preservation, discovery, and reuse of research data in repositories. Among them, eight presented the three main types of metadata, descriptive, structural, and administrative, to enable the preservation of research data in data repositories. We noted limited evidence on how these metadata standards can be used to enhance the discovery and reuse of research data in repositories to enable the preservation, discovery, and reuse of research data in repositories. No reviews indicated specific higher education institutions employing metadata standards for the research data created by their researchers. Repository designs and a lack of expertise and technology know-how were among the challenges identified from the reviewed papers. The review has the potential to influence professional practice and decision-making by stakeholders, including researchers, students, librarians, information communication technologists, data managers, private and public organisations, intermediaries, research institutions, and non-profit organizations.

An Empirical Analysis of the Influence of Different Types of Metadata on the Usefulness of Online Reviews for Healthcare Businesses

This study aims to identify factors that influence the usefulness of online healthcare reviews and to develop a predictive model for review usefulness. A sample of 4,351 online reviews posted between October 2014 and October 2022 was analyzed using negative binomial regression and support vector regression algorithms. The results reveal that user metadata attributes related to reviewer reputation, readability, subjectivity, and containing more sentences have a significant positive influence on review helpfulness. However, reviews assigning higher star ratings to a business are perceived as less useful by healthcare consumers. The study recommends that healthcare businesses should encourage consumers to post reviews, pay attention to the opinions and concerns of high-reputation and cool patients, and use review, business, and user metadata to build effective models for predicting review usefulness. By using a predictive model like the one developed in this study, online review platforms can estimate the helpfulness of new reviews instantly.

Cluster Analysis of Open Research Data: A Case for Replication Metadata

Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR. Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis. I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results. About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility.

Building an annotated corpus for automatic metadata extraction from multilingual journal article references.

Bibliographic references containing citation information of academic literature play an important role as a medium connecting earlier and recent studies. As references contain machine-readable metadata such as author name, title, or publication year, they have been widely used in the field of citation information services including search services for scholarly information and research trend analysis. Many institutions around the world manually extract and continuously accumulate reference metadata to provide various scholarly services. However, manually collection of reference metadata every year continues to be a burden because of the associated cost and time consumption. With the accumulation of a large volume of academic literature, several tools, including GROBID and CERMINE, that automatically extract reference metadata have been released. However, these tools have some limitations. For example, they are only applicable to references written in English, the types of extractable metadata are limited for each tool, and the performance of the tools is insufficient to replace the manual extraction of reference metadata. Therefore, in this study, we focused on constructing a high-quality corpus to automatically extract metadata from multilingual journal article references. Using our constructed corpus, we trained and evaluated a BERT-based transfer-learning model. Furthermore, we compared the performance of the BERT-based model with that of the existing model, GROBID. Currently, our corpus contains 3,815,987 multilingual references, mainly in English and Korean, with labels for 13 different metadata types. According to our experiment, the BERT-based model trained using our corpus showed excellent performance in extracting metadata not only from journal references written in English but also in other languages, particularly Korean. This corpus is available at http://doi.org/10.23057/47.

Annotated Open Corpus Construction and BERT-Based Approach for Automatic Metadata Extraction From Korean Academic Papers

With the accelerating development of science and technology, the academic papers being published in various fields are increasing rapidly. Academic papers specially in science and technology fields are a crucial media for researchers who develop new technologies by identifying knowledge regarding the latest technological trends and conduct derivative studies in science and technology. Therefore, the continual collection of extensive academic papers, structuring of metadata, and construction of databases are significant tasks. However, research on automatic metadata extraction from Korean papers is not being actively conducted currently owing to insufficient Korean training data. We automatically constructed the largest labeled corpus in South Korea to date from 315,320 PDF papers belonging to 503 Korean academic journals and this labeled corpus can be used for training the models of automatic extraction for 12 metadata types from PDF papers. This labeled corpus is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://doi.org/10.23057/48</uri> . Moreover, we developed inspection process and guidelines for the automatically constructed data and performed a full inspection of the validation and testing data. The reliability of the inspected data was verified through the inter-annotator agreement measurement. Using our corpus, we trained and evaluated the BERT based transfer learning model to verify its reliability. Furthermore, we proposed new training methods that can improve the metadata extraction performance of Korean papers, and through these methods, we developed KorSciBERT-ME-J and KorSciBERT-ME-J+C models. The KorSciBERT-ME-J showed the highest performance with an F1 score of 99.36%, as well as robust performance in automatic metadata extraction from Korean academic papers in various formats.

On the role of human and machine metadata in relevance judgment tasks

In order to evaluate the effectiveness of Information Retrieval (IR) systems it is key to collect relevance judgments from human assessors. Crowdsourcing has successfully been used as a method to scale-up the collection of manual relevance judgments, and previous research has investigated the impact of different judgment task design elements (e.g., highlighting query keywords in the document) on judgment quality and efficiency. In this work we investigate the positive and negative impacts of presenting crowd human assessors with more than just the topic and the document to be judged. We deploy different variants of crowdsourced relevance judgment tasks following a between-subjects design in which we present different types of metadata to the human assessor. Specifically, we investigate the effect of human metadata (e.g., what other human assessors think of the current document, as in which relevance level has already been selected by the majority crowd workers), machine metadata (e.g., how IR systems scored this document such as its average position in ranked lists, statistics about the document such as term frequencies). We look at the impact of metadata on judgment quality (i.e., the level of agreement with trained assessors) and cost (i.e., the time it takes for workers to complete the judgments) as well as at how metadata quality positively or negatively impact the collected judgments.

FINT: Flexible In-band Network Telemetry method for data center network

By embedding the state data maintained by the programmable data plane into user packets, the In-band Network Telemetry (INT) can greatly improve the freshness of measured data, such as capturing the micro-bursts in data center networks. However, because the existing INT schemes use fixed telemetry tasks and metadata adding strategies, INT cannot be adjusted in time according to the network state, thus lacking flexibility. To solve the above problems, we propose a Flexible In-band Network Telemetry method FINT. With the designed triple bitmap mechanism, telemetry tasks and parameters can be set dynamically at runtime, which providing flexibility without redeployment to improve telemetry efficiency and reduce the impact of telemetry on network performance. With the designed flexible INT field structure, the types of telemetry metadata carried by user packets can be flexibly selected without significantly adding the indication field. Based on this, to meet the limitation of packet length and reduces the impact on the flow completion time, a greedy telemetry metadata selection algorithm MSG is designed to preferentially select the telemetry metadata with maximum bit-width among the remaining telemetry metadata. The experimental results show that FINT is flexible in adjusting telemetry tasks and add telemetry metadata, and effectively reduce the average flow completion time of the mice flow carrying INT data in the network without significantly increasing the bandwidth consumption.

Narrative Metadata: Leaving the War Metaphor Behind and Unpacking the Control Tactics of Biopower in COVID-19.

Narratives are a veritable type of metadata. Narratives are power-laden storylines that conjure up emotions and enact value systems that markedly affect scientific practices, and to what ends, and for whom science and health innovations are made available. Narratives, if they are left unchecked, can undermine critical thinking and the agency of publics, threatening the possibilities for robust, responsible, relevant, and democratic science. One such narrative, a sociotechnical metadata in its own right, and of immense relevance in the current historical moment of the pandemic, is the uncritical use of the war and other military metaphors in COVID-19 science and planetary health interventions. In October 2022 issue of OMICS, Ebru Yetişkin adopts a biophilosophical transdisciplinary approach and feminist versions of science and technology studies to examine the ways in which the war discourse and other military metaphors have been deployed for the sake of biopower during COVID-19. In this article, we discuss the need to critically unpack the narrative metadata to leave the war metaphor behind, and hold to account the control tactics of biopower embedded in the COVID-19 pandemic.

AI-SPedia: a novel ontology to evaluate the impact of research in the field of artificial intelligence.

Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI. In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indicators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data. To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets. The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.

Look at metatada processing beyond Libraries

Since many years libraries, archives and museums, the institutions entrusted with the dissemination and conservation of cultural heritage, contributed to metada standards definition, meta-dating methodologies and metadata representation in different syntaxes, by partecipating to national projects and international initiatives.Management systems and catalogs used in these contexts borrow from information and telecommunication technology tools, metodoligies and techniques to generate, organize, share and use various types of metadata. But the Information and Communication Technology (ICT) area is not only a supplier of technological tools and solutions as it also constitutes a rich basin in which metadata plays a fundamental role in designing architectures, modeling information systems and implementing services.Through an overview that includes national and European initiatives, especially in the digital transformation process of the public sector, this paper aims to offer a look to metadata beyond the traditional boundary of libraries and other cultural institutions and to underline some relevant aspects such as standardization, sharing, reuse and metadata quality assessment.This framework highlights the need to carry on with the path of cooperation between different functional domains and organizational contexts in order to consolidate and extend the (re)use of metadata schemes, ontologies and controlled vocabularies both in the redesign of digitized processes and in the implementation of services supporting them. By undertaking since design early stages a multidisciplinary approach based on metadata standards can ensure greater flexibility and higher interoperability. This vision requires the enhancement of intersectorial skills that meld metadata methodologies and syntaxes representation basic knowledge with the ability to model functional domains using metadata schemes and ontologies.

Constraints on Future Analysis Metadata Systems in High Energy Physics

In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in an analysis, but designing analysis metadata systems has historically received little direct attention. Among other considerations, an ideal metadata tool should be easy to use by new analysers, should scale to large data volumes and diverse processing paradigms, and should enable future analysis reinterpretation. This document, which is the product of community discussions organised by the HEP Software Foundation, categorises types of metadata by scope and format and gives examples of current metadata solutions. Important design considerations for metadata systems, including sociological factors, analysis preservation efforts, and technical factors, are discussed. A list of best practices and technical requirements for future analysis metadata systems is presented. These best practices could guide the development of a future cross-experimental effort for analysis metadata tools.

SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking

Scientific papers published in journals or conferences, also considered academic publications, are the manifestation of scientific research achievements. Lots of scientific papers published in digital form bring new challenges for academic evaluation and information retrieval. Therefore, research on the ranking method of scientific papers is significant for the management and evaluation of academic resources. In this paper, we first identify internal and external factors for evaluating scientific papers and propose a publication ranking method based on an analysis of a heterogeneous academic network. We use four types of metadata (i.e., author, venue (journal or conference), topic, and title) as vertexes for creating the network; in there, the topics are trained by the SentenceLDA algorithm with the metadata of the abstract. We then use the Gibbs sampling method to create a heterogeneous academic network and apply the ConNetClus algorithm to calculate the probability value of publication ranking. To evaluate the significance of the method proposed in this paper, we compare the ranking results with BM25, PageRank, etc., and homogeneous networks in MAP and NDCG. As shown in our evaluation results, the performance of the method we propose in this paper is better than other baselines for ranking publications.

NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata.

Data management is a critical challenge required to improve the rigor and reproducibility of large projects. Adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) standards provides a baseline for meeting these requirements. Although many existing repositories handle data in a FAIR-compliant manner, there are limited tools in the public domain to handle the metadata burden required to connect data from multi-omic projects that span multiple institutions and are deposited in diverse repositories. One promising approach is the SEEK platform, which allows for diverse metadata and provides an established repository. SEEK is challenged by the assumption of single deposition events where a sample is immutable once entered in the database. This is structured for published data but presents a limitation for ongoing studies where multiple sequential events may occur in a single sample at different sites. To address this issue, we have created a modified wrapper around the SEEK platform that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information. The use of discrete nodes also converts assays from nodes to edges, creating a network model of the study and more accurately representing the experimental process. With these changes to SEEK, users are able to collect and organize the information that researchers need to improve reusability and reproducibility as well as make data and metadata available to the scientific community through public repositories.