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      "description": "Research data rarely go straight from an instrument into a publication. Cleaning, converting, combining, analysing, plotting and writing up are where results take shape, but also where undocumented decisions, ambiguous handovers and manual interventions can make them difficult to trace or repeat. SHAPE introduces five principles for making this processing work explicit, understandable and open to checking: Structure Stages: Define each activity's purpose, inputs, outputs and responsibilities, and make dependencies between stages visible. Harmonize Handovers: Agree on names, formats and meanings so that the next person or tool can interpret and use an output correctly. Automate Actions: Turn recurring actions into executable procedures, recording the settings and decisions needed to run them again. Propagate Provenance: Keep results connected to their inputs, actions, decisions and contributors, and carry that history forward. Evaluate Explicitly: Define acceptance criteria, check inputs and outputs, and review failures and assumptions throughout the work. The five principles apply throughout a workflow; they are not a fixed sequence of steps. Activities such as collecting, cleaning, comparing, combining and collaborating can each involve all five. Adapt the activities to your workflow and revisit the principles whenever inputs, methods, responsibilities or intended uses change. SHAPE complements the FAIR Guiding Principles for findable, accessible, interoperable and reusable research objects and ROOT , which emphasises Robust, Open, Ongoing and Time-tested tools. SHAPE focuses on how processing work is organised so that others, and your future self, can understand, repeat and check it. It condenses established practical advice, such as the ten simple rules for reproducible computational research , good enough practices in scientific computing and The Turing Way , into five questions, and speaks of provenance in the terms of W3C PROV . Each principle acts in its own place: inside each stage, between stages, on every rerun, along the chain and at every checkpoint. SHAPE's nearest neighbours are the FAIR Principles applied to computational workflows and workflow management systems such as Snakemake . They make the automated part of the work findable, reusable and rerunnable. SHAPE also covers the manual steps and handovers around it, and asks whether the work is correct and repeatable: quality and reproducibility, which the FAIR workflow recommendations name as beyond their scope. This deposit contains two complementary A0 portrait posters and a one-page self-check: Framework overview: Introduces the five principles through the problems they prevent, an overview of how they act together in one workflow between FAIR and ROOT, and a card per principle with its guiding question, an abstract diagram, an explanation, practical actions and a line in practice, without relying on a particular discipline or dataset. Workflow example: Applies SHAPE to the creation of the posters themselves. It maps the five principles across authoring, tangling, generation, compilation, checking and packaging, with excerpts from the actual source and build history. Self-check: One A4 page that rates a workflow on the five questions. Level 0 is the problem a principle prevents; levels 1 to 3 are written down, automated and checked. Rating again later shows what changed. Both posters are generated from one Org-mode source using Emacs/Org, Make and LuaLaTeX. The accompanying source bundle includes the source, build instructions, assets, validation results and checksums needed to inspect and rebuild the posters. Each PDF embeds a build-history snapshot captured at compilation; the completed history, including later checking and packaging, is recorded in the repository. The shared release version is 0.9.11 . References Wilkinson, M. D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. doi:10.1038/sdata.2016.18 . Bossert, L. C. (2026). Resilient Technologies. Why Decades-Old Tools Define the ROOT of Modern Research Data Management. Version 1.1.0. Zenodo. doi:10.5281/zenodo.19051651 . Sandve, G. K., Nekrutenko, A., Taylor, J. and Hovig, E. (2013). Ten Simple Rules for Reproducible Computational Research. PLOS Computational Biology, 9(10), e1003285. doi:10.1371/journal.pcbi.1003285 . Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L. and Teal, T. K. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510. doi:10.1371/journal.pcbi.1005510 . Goble, C., Cohen-Boulakia, S., Soiland-Reyes, S., Garijo, D., Gil, Y., Crusoe, M. R., Peters, K. and Schober, D. (2020). FAIR Computational Workflows. Data Intelligence, 2(1–2), 108–121. doi:10.1162/dint_a_00033 . Wilkinson, S. R., Aloqalaa, M., Belhajjame, K. et al. (2025). Applying the FAIR Principles to computational workflows. Scientific Data, 12, 328. doi:10.1038/s41597-025-04451-9 . The Turing Way Community (2025). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo. doi:10.5281/zenodo.3233853 . Mölder, F., Jablonski, K. P., Letcher, B. et al. (2021). Sustainable data analysis with Snakemake. F1000Research, 10, 33. doi:10.12688/f1000research.29032.2 . Lebo, T., Sahoo, S. and McGuinness, D. (eds.) (2013). PROV-O: The PROV Ontology. W3C Recommendation, 30 April 2013. https://www.w3.org/TR/prov-o/ .",
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      "description": "NFDI4ING is the National Research Data Infrastructure for the Engineering Sciences. To educate Research Data Management (RDM), NFDI4ING's archetype Fiona conducts a summer school as practice-oriented training. Following the slogan \"Driving RDM\", a small-scale data-driven engineering experiment is conducted to theoretically learn and practically apply RDM. Clamping blocks, gears and measurement technology in your pocket are leveraged. Slides from the modules and related labs are published. This publication record contains the RDM Career Paths and FAIR4Studium module. It introduces professional roles and career opportunities in research data management, including Data Stewards, Data Curators, and Research Software Engineers. The accompanying exercises address practical RDM skills such as file organization, naming conventions, version control, backups, responsible data sharing, and literate programming, including the ROOT principles for resilient digital technologies.",
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      "title": "Titanic Passengers and Crew: Demographics and Survival Data",
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      "description": "This dataset provides five comma-separated files for exploring Titanic survival: individual records for 2,207 people , outcome counts by passenger class or crew membership and sex, and survival summaries using three-year age bands . It is intended for teaching, descriptive analysis, and data visualisation. Files and format titanic.csv : 2,207 person-level records, with five columns. titanic_by_class.csv : four passenger-class/crew groups, with four columns. titanic_by_sex.csv : four passenger-class/crew groups with separate counts by sex, with seven columns. titanic_by_age_and_class.csv : 100 three-year age-band/class combinations, with five columns. titanic_by_age.csv : 25 three-year age groups, with three columns. All five files use UTF-8 encoding, comma separators, one header row, and LF line endings. Decimal values use a full stop. Row counts exclude the header. NA marks unknown ages and undefined percentages in empty age/class cells; NaN is used for an empty age-only group's percentage. Neither marker means zero. The accompanying SHA256SUMS file contains a SHA-256 checksum for each of the five CSV files. Person-level data: titanic.csv class : passenger class or crew membership; values are 1st , 2nd , 3rd , and Crew . sex : sex as recorded in the source; female or male . age : reported age in years at the sinking, including fractional infant ages. Two records have an unknown age, represented by NA . survived : survival outcome; 1 means survived and 0 means did not survive. embarked : embarkation location; B = Belfast, C = Cherbourg, Q = Queenstown, and S = Southampton. The file contains 1,317 passengers and 890 crew members: 324 first-class, 284 second-class, and 709 third-class passengers. There are 711 survivors and 1,496 people who did not survive. Recorded sex counts are 489 female and 1,718 male. The four source crew categories are combined into Crew , and the source field gender is named sex in this export. Names and identifiers are omitted. Identical rows are retained because different people can share the same five attributes. Ages have not been imputed or grouped in this file; the other columns contain no missing values. Rows are sorted by class ( 1st , 2nd , 3rd , Crew ), sex ( female , male ), numeric age ascending, survival outcome ( 0 , 1 ), and embarkation code alphabetically. Missing ages follow known ages within each class/sex group. Outcomes by class and crew: titanic_by_class.csv Class : passenger class or crew membership; rows are ordered 1st , 2nd , 3rd , Crew . Aboard : number of people in the group. Survived : number of survivors in the group. Died : number who did not survive, calculated as Aboard - Survived . Both sexes and all ages are included, including the two people with unknown ages. Counts sum to 2,207 aboard, 711 survived, and 1,496 died. Each row satisfies Aboard = Survived + Died . Crew is a separate group, not a fourth passenger class. Outcomes by class and sex: titanic_by_sex.csv Class : the same four ordered class/crew groups. Women aboard , Women survived , Women died : counts for records with sex = female . Men aboard , Men survived , Men died : counts for records with sex = male . The column labels “Women” and “Men” refer to the recorded sex categories and include children. Unknown ages are retained. For each class/crew group, adding the corresponding female and male counts reproduces titanic_by_class.csv . Counts cover all 2,207 people; a missing class/sex combination would be represented by zero counts. Age and class summary: titanic_by_age_and_class.csv age_midpoint : integer age-band label, taking the values 1, 4, 7, …, 73 . class_code : 1 = first class, 2 = second class, 3 = third class, and 4 = crew. survival_pct : 100 × survivors / n , rounded to six decimal places; NA when the group is empty. n : number of people with known age in the group. survivors : number of survivors in the group. The summary crosses 25 age bands with four class/crew categories. Bands are [0,3) , [3,6) , …, [72,75) : each includes its lower bound and excludes its upper bound. Both sexes are pooled. The two people with unknown ages are excluded, leaving 2,205 people and 711 survivors . Despite its name, age_midpoint contains the middle completed-year age, rather than the exact midpoint of the continuous interval. For example, label 1 represents ages 0–2, including fractional ages below three; label 4 represents ages 3–5. No individual ages are rounded before grouping. All 100 combinations are retained, including 12 empty cells with n = 0 , survivors = 0 , and survival_pct = NA . These cells must not be interpreted as zero-percent survival. Rows are ordered by increasing age-band label and then class code. Group counts, survivor counts, and percentages have been checked against the person-level file. Age-only summary: titanic_by_age.csv Age : a completed-year age-band label: 0-2 , 3-5 , …, 72-74 . People : number of people with known age in the band. Survived % : percentage who survived, rounded to one decimal place; NaN for an empty band. This summary pools passengers and crew, all classes, and both sexes. Its 25 ordered groups use the same three-year intervals as titanic_by_age_and_class.csv , including fractional ages without rounding them first. There is no combined 60+ group. Unknown ages are excluded, and counts sum to 2,205. Within each age band, summing the four class/crew counts in the age/class summary gives People ; the survival percentage is calculated from pooled survivor and person counts, not by averaging the four class percentages. Source and preparation The preparation notes identify the direct source as the Titanic CSV in Dennis Sun’s Principles of Data Science repository , accessed on 25 September 2026. Selected fields were manually transcribed from the browser-readable CSV and exported programmatically; the person-level export was subsequently sorted as described above. The source URL refers to a mutable branch rather than a pinned revision. A related 2,207-person dataset is documented in the stablelearner reference manual . The exact transformation history linking that dataset to the educational CSV has not been independently established. This collection contains individual records and is distinct from the familiar 2,201-person Titanic contingency table. Interpretation and limitations These are descriptive data for the supplied cohort, not a definitive historical reconstruction. Reported ages may be uncertain. A complete record-by-record comparison with the upstream CSV has not been performed, so transcription errors remain possible despite internal consistency checks. The exact upstream revision, upstream licensing requirements, and any transcription correction log remain to be established. Some age/class groups contain very few people; percentages should therefore be interpreted alongside n . The summaries contain no uncertainty estimates. Class codes identify categories, and crew is not a fourth passenger class. Pooling sexes and crew roles can conceal differences within groups. The data alone do not establish causal effects of age or class on survival.",
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      "title": "ROOT — Technology That Stands the Test of Time",
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      "citation": "Bossert, Lukas C.. 2026-09-15. ROOT — Technology That Stands the Test of Time. Version 1.0.0. https://doi.org/10.5281/zenodo.22772124",
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      "language": "",
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      "contributors": [
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        {
          "name": "Dirier, Zeynep",
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        {
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        {
          "name": "Scholten-Strauch, Ines",
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        },
        {
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      "description": "Readme Phyphox™ (properly spelled phyphox, short for “physical phone experiments”) is an app developed and used at RWTH Aachen University for physics education (Lahme et al., 2025 [1]; Staacks et al., 2018 [2]). The app enables easy acquisition and analysis of data using the sensors of students’ own smartphones and thereby promotes low-barrier, authentic experimentation both inside and outside traditional laboratory settings. A central feature of phyphox is the live visualization of incoming sensor data on the smartphone display, including, for example, real-time plots, derived quantities, and automated analysis routines. Students can therefore directly observe how physical processes translate into measurement signals while performing the experiment itself. This real-time monitoring of sensor data supports exploratory and self-directed experimentation because measurements remain interpretable and controllable immediately during acquisition, even before they are uploaded to a shared repository or formally embedded into a theoretical framework. Building on these possibilities, phyphox advocates a measurement-before-theory approach in its teaching materials, suggesting that students may first conduct experiments “blind,” i.e., without prior theoretical explanation, and only later derive and discuss the corresponding physics concepts from the jointly collected data (see phyphox modular online worksheets [3]). This approach is intended to strengthen exploratory learning, student agency, and the connection between empirical observation and theoretical understanding. The same educational idea has also been taken up beyond the official phyphox project itself, for example on the German higher-education platform e-teaching.org , where phyphox-based experiments are described as opportunities for students to conduct experiments before deriving the associated theory. By explicitly comparing this situation to scientific research itself (“ähnlich der Forschung”), e-teaching.org reframes incomplete theoretical understanding as a productive and motivating feature of scientific inquiry and research [4]. Here we put these ideas of exploratory—impromptu—measurement to the test by analysing data from a repetitive “swish swish” hand-wave gesture performed near the smartphone’s light sensor. The data was acquired spontaneously during a Zoom™ call on 13 February 2026 and subsequently uploaded to the sciebo repository of RWTH Aachen University [5]. We provide (i) the original phyphox dataset (“Licht 2026-02-13 13-34-38.xls”), (ii) an upsampled and annotated version thereof (“preview_resampled_annotated.xlsx”), and (iii) the lecture slides used for presentation (“Module_phyphoxSNR.pptx”). Acknowledgement: The author would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4ING consortium. Funded by the German Research Foundation (DFG) - project number 442146713. References [1] DOI: 10.1103/bwpv-z3xh [2] DOI: 10.1088/1361-6552/aac05e [3] URL: phyphox.org/de/module/ [4] URL: e-teaching.org/community/digital-learning-map/phyphox-physical-phone-experiments [5] URL: rwth-aachen.sciebo.de/login All links were accessed on the day of publication.",
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      "title": "DALIA Federated Curation Concept",
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          "affiliation": "Nationale Forschungsdateninfrastruktur (NFDI) e.V.",
          "type": "Editor",
          "orcid": "0000-0002-4522-1099"
        },
        {
          "name": "Groth, Paul",
          "affiliation": "University of Amsterdam",
          "type": "Editor",
          "orcid": "0000-0003-0183-6910"
        }
      ],
      "date": "2025-08-04",
      "datePrecision": "day",
      "event": {
        "title": "2nd Conference on Research Data Infrastructure",
        "acronym": "CoRDI",
        "dates": "26-28 August 2025",
        "place": "Aachen, Germany",
        "url": "https://www.nfdi.de/cordi-2025/",
        "session": "8. Educating"
      },
      "doi": "10.5281/zenodo.16736346",
      "conceptDOI": "10.5281/zenodo.16736345",
      "version": "",
      "keywords": [
        "Data Literacy",
        "Research Data Management",
        "OER",
        "Search Platform",
        "Curation"
      ],
      "description": "In the rapidly expanding digital research landscape, curation is crucial for sustaining high-quality Open Educational Resources (OER) especially in research data management (RDM). As OER ecosystems grow, the proliferation of both human- and machine-generated content presents significant challenges for maintaining discoverability and educational relevance. The DALIA portal, a specialized search engine for OER on data literacy, is designed to ensure that all indexed resources meet high educational and technical standards across diverse scientific domains. This contribution examines how a comprehensive federated curation approach—drawing on distributed expertise from various scientific communities, National Research Data Infrastructure (NFDI) consortia, data competency centers, and federal RDM initiatives—will provide the foundation for DALIA's quality assurance framework. Unlike traditional centralized curation models, DALIA's federated approach will enable decentralized sourcing while ensuring semantic coherence and metadata integrity across platforms. This system will overcome the limitations of centralized quality control by leveraging domain-specific knowledge from multiple scientific communities. It will support customizable metadata schemas, such as the DALIA Interchange Format [1], which accommodate discipline-specific terminology, and community-specific collections. Additionally, collaborative curation tools will enable community-driven quality assessment. Through these mechanisms, scientific communities will actively participate in building and maintaining high-quality educational resource collections tailored to their specific needs. DALIA's comprehensive quality assurance system will integrate both AI-enhanced capabilities and API-driven content acquisition to create a sophisticated curation ecosystem. A key innovation will be the embedding of artificial intelligence in the curation interface to assist subject-matter experts in efficiently classifying, annotating, and enhancing resource metadata. This AI augmentation will include semantic tagging, content summarization, quality assessment prompts, and cross-referencing with existing taxonomies. Simultaneously, standardized APIs and persistent resource identifiers will automatically harvest metadata from source repositories, pre-populating curation forms with essential information. Comparative evaluation of leading OER platforms will reveal that without robust curation, even large-scale repositories fail to deliver relevant results for nuanced or interdisciplinary topics. In contrast, DALIA's approach will significantly improve search precision, allowing users to retrieve contextually aligned, pedagogically appropriate materials more reliably. By positioning this federated curation approach at the core of its infrastructure, DALIA will develop from a basic aggregator into an intelligent educational resource hub that actively supports research data management education. This work will highlight curation as a foundational layer of OER infrastructure—one that must evolve in tandem with the accelerating pace of automated content generation. It also advocates for the strategic integration of human-AI collaboration in curation workflows as a necessary response to the growing complexity and scale of educational resource management in the digital era. Currently, DALIA indexes a wide spectrum of data literacy tools, training materials, and best practices, and welcomes materials from all sides, nationally and also internationally. The platform is under continuous development with a strong focus on community engagement. Researchers, educators, data stewards, and professionals from libraries and archives are invited to contribute content, help shaping metadata standards, and provide feedback to ensure that DALIA stays responsive, inclusive, and aligned with the evolving data literacy landscape. By fostering collaboration and co-creation, DALIA strives to become a sustainable pillar of the academic data culture.",
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        "Research Data Management",
        "OER",
        "Search Platform",
        "Curation"
      ],
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        "topic:keyword:2c4fe4bf73b64cc01f",
        "topic:metadata"
      ],
      "currentRelease": null,
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      "citation": "Herres-Pawlis, Sonja; Zubilewicz, Maciej; Steiner, Petra; Schrade, Torsten; Hüppe, Henrika; Bossert, Lukas; Politze, Marius; Müller, Matthias. 2025-08-04. Safeguarding Quality of OER Materials in DALIA: A Federated Curation Concept. https://doi.org/10.5281/zenodo.16736346",
      "bibtex": "@misc{work-zenodo-16736345,\n  title = {Safeguarding Quality of OER Materials in DALIA: A Federated Curation Concept},\n  author = {Herres-Pawlis, Sonja and Zubilewicz, Maciej and Steiner, Petra and Schrade, Torsten and Hüppe, Henrika and Bossert, Lukas and Politze, Marius and Müller, Matthias},\n  year = {2025},\n  date = {2025-08-04},\n  doi = {10.5281/zenodo.16736346},\n  url = {https://doi.org/10.5281/zenodo.16736346},\n  note = {Publication}\n}\n",
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          "name": "Bossert, Lukas C.",
          "affiliation": "RWTH Aachen University",
          "orcid": "0000-0003-3076-3968"
        }
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      "contributors": [],
      "date": "2025-06-06",
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      "description": "This project is about the Datenkompetenzzentren (DKZ) / Data Competence Centers. It analysis its connections between entities and visualizes those on a map. Please note the link to the repository: https://git.rwth-aachen.de/dl/best-practices/dkz-network-analysis/ Data The data used for the network has been taken from Wikidata using two queries: - `wikidata-edges.sparql` - `wikidata-nodes.sparql` The results from the Wikidata querries are stored in: - `dkz-network-analysis-edges.csv` - `dkz-network-analysis-nodes.csv` Visualization We used `Gephi 0.10.1` for the visualization. The `.gephi`-file is also provided. You find all maps as layers in `dkz-network-analysis-map.svg`. Versioning 0.0.2: change funding logo Licence The maps are licenced under CC-BY: CC-BY Lukas C. Bossert @ RWTH Aachen & DKZ.2R",
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    {
      "id": "work:zenodo:7966673",
      "family": "7966673",
      "recordId": "15282166",
      "title": "Human intestinal Bacteria Collection (HiBC): Isolates and genomes metadata",
      "language": "",
      "type": "Dataset",
      "subtype": "dataset",
      "authors": [
        {
          "name": "Hitch, Thomas C. A",
          "affiliation": "University Hospital of RWTH Aachen",
          "orcid": "0000-0003-2244-7412"
        },
        {
          "name": "Masson, Johannes M.",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Pauvert, Charlie",
          "affiliation": "University Hospital of RWTH Aachen",
          "orcid": "0000-0001-9832-2507"
        },
        {
          "name": "Bosch, Johanna",
          "affiliation": "University Hospital of RWTH Aachen",
          "orcid": "0000-0003-3982-849X"
        },
        {
          "name": "Nüchtern, Selina",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Treichel, Nicole",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Razavi, Soheila",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Baloh, Marko",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Afrizal, Afrizal",
          "affiliation": "University Hospital of RWTH Aachen",
          "orcid": "0000-0001-8738-9706"
        },
        {
          "name": "Kousetzi, Ntana",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Aguirre, Andrea M.",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Wylensek, David",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Coates, Amy",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Jennings, Susan A. V.",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Panyot, Atscharah",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Viehof, Alina",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Schmitz, Matthias A.",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Stuhrmann, Maximilian",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Deis, Evelyn C.",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Bisdorf, Kevin",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Cramer, Thorsten",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Lissin, Arthur",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures"
        },
        {
          "name": "Schober, Isabel",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures"
        },
        {
          "name": "Witte, Julius",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures"
        },
        {
          "name": "Riedel, Thomas",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures"
        },
        {
          "name": "Wende, Marie",
          "affiliation": "Helmholtz Centre for Infection Research"
        },
        {
          "name": "Winter, Katrin A.",
          "affiliation": "Helmholtz Centre for Infection Research"
        },
        {
          "name": "Riva, Alessandra",
          "affiliation": "University of Vienna"
        },
        {
          "name": "Trinh, Stefanie",
          "affiliation": "University Hospital of RWTH Aachen"
        },
        {
          "name": "Mitchell, Laura",
          "affiliation": "University of Nottingham"
        },
        {
          "name": "Hartman, Jonathan",
          "affiliation": "RWTH Aachen University"
        },
        {
          "name": "Berry, David",
          "affiliation": "University of Vienna"
        },
        {
          "name": "Seitz, Jochen",
          "affiliation": "University of Duisburg-Essen"
        },
        {
          "name": "Bossert, Lukas C.",
          "affiliation": "RWTH Aachen University",
          "orcid": "0000-0003-3076-3968"
        },
        {
          "name": "Allers, Thorsten",
          "affiliation": "University of Nottingham"
        },
        {
          "name": "Strowig, Till",
          "affiliation": "Helmholtz Centre for Infection Research"
        },
        {
          "name": "Abt, Birte",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures"
        },
        {
          "name": "Reimer, Lorenz C.",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures",
          "orcid": "0000-0002-7805-0660"
        },
        {
          "name": "Overmann, Jörg",
          "affiliation": "Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures",
          "orcid": "0000-0003-3909-7201"
        },
        {
          "name": "Clavel, Thomas",
          "affiliation": "University Hospital of RWTH Aachen",
          "orcid": "0000-0002-7229-5595"
        }
      ],
      "contributors": [],
      "date": "2025-04-25",
      "datePrecision": "day",
      "event": null,
      "doi": "10.5281/zenodo.15282166",
      "conceptDOI": "10.5281/zenodo.7966673",
      "version": "",
      "keywords": [
        "metadata",
        "human intestine",
        "gut microbiome",
        "bacteria",
        "anaerobe",
        "cultivation"
      ],
      "description": "The Human intestinal Bacteria Collection (HiBC) is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly. This dataset includes the taxonomy of the isolates, as well as metadata regarding their cultivation and isolation. We also provide metadata regarding the sequencing, genome assembly process and the biological sequences. UPDATE v7 : INSDC accession for Segatella sinensis CLA-AA-H117 was a missing value and is now the correct value of GCA_040324585.2. UPDATE v6: The growth atmosphere is now indicated by anaerobic or aerobic instead of \"Anaerobe/Aerobe\" that was a misleading term. The risk group of these two isolates went from 1 to 2: CLA-AA-H205: Anaerostipes caccae CLA-AA-H83: Bacteroides fragilis The risk group of the following isolates has been updated (usually from unknown to 1, or from 2 to 1): CLA-SR-H026: Aedoeadaptatus acetigenes CLA-KB-H139: Bacteroides xylanisolvens CLA-SR-H015: Bacteroides xylanisolvens CLA-AA-H187: Blautia fusiformis CLA-AA-H274: Brotaphodocola catenula CLA-AA-H286: Butyricimonas faecihominis CLA-AA-H278: Clostridium fessum CLA-AA-H147: Dorea ammoniilytica CLA-SR-H027: D orea formicigenerans CLA-KB-H89: Dorea longicatena CLA-KB-H94: Dorea longicatena CLA-SR-H022: Enterococcus lactis CLA-AA-H250: Hominenteromicrobium mulieris CLA-AA-H232: H ominilimicola fabiformis CLA-AA-H246: Hominisplanchenecus faecis CLA-AA-H276: Hominiventricola filiformis CLA-AA-H213: Oliverpabstia intestinalis CLA-AA-H241: Oliverpabstia intestinalis CLA-AA-H58: Pilosibacter fragilis CLA-KB-H110: Ruthenibacterium lactatiformans CLA-AA-H174: Segatella sinensis CLA-AA-H2: Veillonella parvula CLA-AA-H273: Waltera acetigignens Typos in media list have been fixed. UPDATE v5 : The accessions number for the genomes on INSDC databases are added under the column Accession. Plus two typos in the risk group column have been corrected as follow: CLA-AA-H173: from Risk Group 4 (!) to 2 like the other strain of Sutterella wadsworthensis CLA-AA-H198: from Risk Group 4 (!) to 1 like the other Bifidobacterium species. UPDATE v4 : Only the taxonomy of a couple of isolates has been changed, as follow: CLA-ER-H4: Collinsella sp900547855 instead of Collinsella sp900544645 CLA-AA-H142: Pilosibacter fragilis ( f__Clostridiaceae ) instead of Sakamotonia hominis gen. nov. ( f__Lachnospiraceae ) CLA-AA-H58: Pilosibacter fragilis ( f__Clostridiaceae ) instead of Sakamotonia hominis gen. nov. ( f__Lachnospiraceae ) CLA-AA-H89B: Lachnospira intestinalis sp. nov. instead of Lachnospira hominis sp. nov. CLA-JM-H10: Lachnospira hominis sp. nov. instead of Lachnospira intestinalis sp. nov. CLA-JM-H7B: Faecalibacterium taiwanense instead of Faecalibacterium faecis sp. nov. CLA-JM-H45: Merdimmobilis hominis instead of Hominicola intestinalis gen. nov. UPDATE v3 : The genome of one of our isolate had been unfortunately swapped. This mistake has been now corrected on Zenodo and Coscine. The genome of Segatella sinensis CLA-AA-H117 should be considered correct with 103 contigs and 3 671 232 nt. Please note that the genome available at the NCBI is the correct one (GCA_040324585.2). Two typos regarding taxonomy have been corrected as well: Maccoya intestinihominis has been corrected to Maccoyia intestinihominis and Faecousia faecis to Faecousia intestinalis .",
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            {
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            {
              "name": "Marianne Grognot"
            },
            {
              "name": "Thorsten Allers"
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            {
              "name": "Till Strowig"
            },
            {
              "name": "Michael Pester"
            },
            {
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            {
              "name": "Jörg Overmann"
            },
            {
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            }
          ],
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        }
      ],
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        "topic:keyword:b277fd623676a525c2",
        "topic:keyword:cf67e05aa26a06c74d",
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          "name": "Schmitt, Robert",
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