SampleDB: A sample and measurement metadata database

Rhiem, Florian

doi:10.21105/joss.02107

Cited by 3 publications

(4 citation statements)

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“…An alternative ELN, Chemotion 26, 27 , is more focused on the chemistry domain. Note, though, that the ELN Consortium 28 , involving ELNs and RDM-related works including Chemotion, eLabFTW, Chemedata 29, 30 , Herbie 31 , Juliabase 32 , Kadi4Mat 33 , PASTA-ELN 34 , and SampleDB 35, 36 , aims at making ELN entries interchangeable via the ELN data format 28 .…”

Section: Discussionmentioning

confidence: 99%

LISTER: Semi-automatic metadata extraction from annotated experiment documentation in eLabFTW

Musyaffa

Rapp

Gohlke

2023

Preprint

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The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). For that, the annotation of research data with metadata is central. However, existing research data management workflows often require that metadata should be created by the corresponding researchers, which takes effort and time. Here, we developed LISTER as a methodological and algorithmic solution to disentangle the creation of metadata from ontology alignment and extract metadata from annotated template-based experiment documentation using minimum effort. We focused on tailoring the integration between existing platforms by using eLabFTW as the electronic lab notebook and adopting the ISA (investigation, study, assay) model as the abstract data model framework; DSpace is used as a data cataloging platform. LISTER consists of three components: customized eLabFTW entries using specific hierarchies, templates, and tags; a 'container' concept in eLabFTW, making metadata of a particular container content extractable along with its underlying, related containers; a Python-based app to enable easy-to-use, semi-automated metadata extraction from eLabFTW entries. LISTER outputs metadata as machine-readable .json and human-readable .csv formats, and MM descriptions in .docx format that could be used in a thesis or manuscript. The metadata can be used as a basis to create or extend ontologies, which, when applied to the published research data, will significantly enhance its value due to a more complete and holistic understanding of the data, but might also enable scientists to identify new connections and insights in their field. We applied LISTER to the fields of computational biophysical chemistry as well as protein biochemistry and molecular biology, and our concept should be extendable to other life science areas.

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Section: Discussionmentioning

confidence: 99%

LISTER: Semi-automatic metadata extraction from annotated experiment documentation in eLabFTW

Musyaffa

Rapp

Gohlke

2023

Preprint

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“…An alternative ELN, Chemotion, 24,25 is more focused on the chemistry domain. Note, though, that the ELN Consortium, 26 involving ELNs and RDM-related works including Chemotion, eLabFTW, Chemedata, 27,28 Herbie, 29 Juliabase, 30 Kadi4Mat, 31 PASTA-ELN, 32 and SampleDB, 33,34 aims at making ELN entries interchangeable via the ELN data format. 26 The choice of the ISA Model reflects its support in the communities of life sciences, biomedicine, and environmental research 8 and its straightforward and realistic structuring of heterogeneous experiments.…”

Section: Approachmentioning

confidence: 99%

“…An alternative ELN, Chemotion, , is more focused on the chemistry domain. Note, though, that the ELN Consortium, involving ELNs and RDM-related works including Chemotion, eLabFTW, Chemedata, , Herbie, Juliabase, Kadi4Mat, PASTA-ELN, and SampleDB, , aims at making ELN entries interchangeable via the ELN data format…”

Section: Approachmentioning

confidence: 99%

LISTER: Semiautomatic Metadata Extraction from Annotated Experiment Documentation in eLabFTW

Musyaffa,

Rapp,

Gohlke

2023

J. Chem. Inf. Model.

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The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). For that, the annotation of research data with metadata is central. However, existing research data management workflows often require that metadata be created by the corresponding researchers, which takes effort and time. Here, we developed LISTER as a methodological and algorithmic solution to create and extract metadata from annotated, template-based experimental documentation using minimum effort. We focused on tailoring the integration between existing platforms by using eLabFTW as the electronic lab notebook and adopting the ISA (investigation, study, assay) model as the abstract data model framework. LISTER consists of four components: annotation language to support metadata extraction; customized eLabFTW entries using specific hierarchies, templates, and tags to structure reusable scientific documentation; a "container" concept in eLabFTW, making metadata of a particular container content extractable along with its underlying, related experiments via a single click; a Python-based app to enable easy-to-use, semiautomated metadata extraction from eLabFTW entries. LISTER outputs metadata in machine-readable .json and human-readable .xlsx formats, and Material and Methods (MM) descriptions in .docx format that could be used in a thesis or manuscript. The metadata can be used as a basis to create or extend ontologies, which, when applied to the published research data, will significantly enhance its value. DSpace is used as a data cataloging platform for hosting the extracted metadata and research data. We applied LISTER to computational biophysical chemistry, protein biochemistry, and molecular biology, and our concept should be extendable to other life science areas.

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“…The open-source, web-based research metadata database and electronic lab notebook SampleDB [2] allows the definition of individual metadata schemas fitted to specific environments and use cases. These schemas allow the description of processspecific metadatasets using various datatypes and conditions, that have to be met by a new record and are validated on creation.…”

mentioning

confidence: 99%

Exchanging Research Data with SampleDB

Deckers,

Rhiem

2023

Proc Conf Res Data Infrastr

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Exchanging research data between systems is a vital task in research data management, especially in the context of collaborations. Therefore mapping data into standardized data descriptions and offering interfaces for efficient data exchange are required. The metadata database and electronic lab notebook SampleDB supports various ways of exporting data to other services, such as Dataverse, SciCat, and JupyterHub, as well as file exports in open formats. The concept of SampleDB federations allows data exchange in loosely coupled federations of SampleDB instances.

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SampleDB: A sample and measurement metadata database

Cited by 3 publications

References 1 publication

LISTER: Semi-automatic metadata extraction from annotated experiment documentation in eLabFTW

LISTER: Semi-automatic metadata extraction from annotated experiment documentation in eLabFTW

LISTER: Semiautomatic Metadata Extraction from Annotated Experiment Documentation in eLabFTW

Exchanging Research Data with SampleDB

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