Correction Of Raw Affiliation For A Publication From Sorbonne Université

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This article addresses the necessary correction for the raw affiliation string associated with a publication originating from Sorbonne Université and its affiliated institutions. The accurate representation of institutional affiliations is crucial for maintaining data integrity, ensuring proper attribution, and facilitating the tracking of research output. This correction involves disambiguating and properly assigning affiliations to the relevant research entities, including Sorbonne Université, Paris Brain Institute, Inserm, CNRS, INRIA, APHP, CATI, CEA, Institut de la Vision, and Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts. The correction ensures that the publication accurately reflects the contributions of each institution, thereby enhancing the discoverability and impact of the research.

H2: The Importance of Accurate Affiliation Data

In academic publishing, accurate affiliation data is paramount. When researchers submit their work, the affiliations they provide serve as a crucial link between the research and the institutions that supported it. These affiliations are used in various ways, including:

  • Attribution: Correctly attributing research to the appropriate institutions ensures that they receive due credit for their contributions. This is vital for institutional rankings, funding opportunities, and overall reputation.
  • Discoverability: Accurate affiliations help readers and other researchers find publications affiliated with specific institutions. This is particularly important for those seeking expertise or collaborations within a particular field.
  • Data Analysis: Bibliometric analyses and research evaluations rely heavily on affiliation data. Incorrect or ambiguous affiliations can skew results and lead to inaccurate assessments of research impact.
  • Funding and Grants: Many funding agencies track research output based on institutional affiliations. Accurate data is essential for demonstrating the return on investment in research funding.

Given these critical functions, the need for correcting raw affiliation strings becomes evident. Raw affiliation data often contains inconsistencies, abbreviations, and variations in naming conventions, making it difficult to parse and analyze accurately. This article focuses on a specific instance where the raw affiliation string from a publication affiliated with Sorbonne Université and associated institutions requires correction to ensure data integrity and proper attribution. The process of correcting this affiliation involves identifying the various institutions mentioned, disambiguating their relationships, and assigning the appropriate identifiers to each.

H2: Identifying the Raw Affiliation String and Associated Institutions

The raw affiliation string in question originates from a publication with contributions from researchers affiliated with Sorbonne Université and several other prominent institutions. The string, as provided, is as follows:

"From the Sorbonne Université (G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D.), Paris Brain Institute, Inserm, CNRS, INRIA, APHP; CATI (C.F., M.C., J.-F.M.), US52-UAR2031, CEA, Paris Brain Institute, Sorbonne Université, CNRS, INSERM, APHP; Sorbonne Université (M.N., I.A.), Inserm, CNRS, Institut de la Vision; Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts (M.N., I.A.), National Rare Disease Center REFERET and INSERM-DGOS CIC 1423;..."

Upon initial inspection, several institutions are identifiable within this string. These include:

  • Sorbonne Université: A leading research university in Paris, France.
  • Paris Brain Institute: A renowned neuroscience research center.
  • Inserm (Institut National de la Santé et de la Recherche Médicale): The French National Institute of Health and Medical Research.
  • CNRS (Centre National de la Recherche Scientifique): The French National Centre for Scientific Research.
  • INRIA (Institut National de Recherche en Informatique et en Automatique): The French National Institute for Research in Digital Science and Technology.
  • APHP (Assistance Publique – Hôpitaux de Paris): The Paris public hospital system.
  • CATI (Centre d'Acquisition et de Traitement des Images): An image acquisition and processing center.
  • CEA (Commissariat à l'énergie atomique et aux énergies alternatives): The French Alternative Energies and Atomic Energy Commission.
  • Institut de la Vision: A vision research institute.
  • Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts: A national ophthalmology hospital.
  • National Rare Disease Center REFERET: A center specializing in rare diseases.
  • INSERM-DGOS CIC 1423: A clinical investigation center.

The presence of abbreviations, multiple listings, and varying levels of detail necessitates a careful approach to correcting this raw affiliation string. Each institution must be accurately identified and linked to the appropriate researchers and publications. This process ensures that the contributions of each institution are correctly acknowledged and that the research output is accurately represented in databases and repositories.

H2: Disambiguation and Correction Process

The disambiguation and correction process involves several key steps to ensure the accurate representation of institutional affiliations. The primary goal is to resolve any ambiguities and standardize the affiliation data, allowing for precise attribution and analysis. Here’s a breakdown of the process:

  1. Identification of Institutions: The initial step involves identifying all institutions mentioned in the raw affiliation string. This includes recognizing variations in naming, abbreviations, and acronyms.
  2. Standardization of Names: Once identified, the names of the institutions must be standardized. This involves using the official name of the institution and avoiding abbreviations or variations.
  3. Use of ROR Identifiers: The Research Organization Registry (ROR) identifiers are crucial for disambiguation. Each institution is assigned a unique ROR ID, which helps to differentiate between organizations with similar names or those that have undergone name changes. The provided new_rors and previous_rors lists are essential in this step.
  4. Assignment of RORs: Assign the correct ROR identifiers to each institution. The provided new_rors values include:
    • Sorbonne Université (02vjkv261)
    • Paris Brain Institute (02feahw73)
    • Inserm (02en5vm52)
    • CNRS (00dcv1019)
    • INRIA (00jjx8s55)
    • APHP (000zhpw23)
  5. Linking Researchers to Affiliations: Associate the researchers listed (e.g., G.C., C.D.-F., E.P.) with their respective institutions. This often requires additional information from the publication or other sources.
  6. Addressing Complex Affiliations: Some researchers may have multiple affiliations. In such cases, it is important to list all relevant institutions and ensure that the affiliations are accurately represented.
  7. Verification and Validation: The final step involves verifying the corrected affiliations and validating the data against external sources. This ensures the accuracy and completeness of the corrected affiliation string.

The provided information indicates a need to correct the raw affiliation string to accurately reflect the affiliations of the researchers and institutions involved. The use of ROR identifiers is a critical component of this process, ensuring that each institution is uniquely and correctly identified. This correction process not only benefits the specific publication in question but also contributes to the overall quality and reliability of research data.

H2: Applying ROR Identifiers for Accurate Attribution

Applying ROR identifiers is a critical step in ensuring accurate attribution of research to the correct institutions. ROR (Research Organization Registry) identifiers are unique, persistent identifiers for research organizations worldwide. They provide a standardized way to identify and disambiguate institutions, which is particularly important in cases where institution names may vary slightly or have undergone changes over time. In the context of correcting the raw affiliation string from Sorbonne Université, ROR identifiers play a pivotal role in several ways:

  • Unambiguous Identification: ROR IDs eliminate ambiguity by providing a unique identifier for each institution. For example, Sorbonne Université has the ROR ID 02vjkv261, which distinguishes it from other institutions with similar names.
  • Tracking Affiliations Over Time: Institutions may merge, split, or change names. ROR identifiers help track these changes by maintaining a persistent link to the institution, even if its name has changed.
  • Data Integration: ROR IDs facilitate the integration of research data across different databases and systems. This is crucial for bibliometric analysis, research evaluation, and tracking the impact of research output.
  • Improved Discoverability: By using ROR IDs, researchers and institutions can ensure that their work is accurately attributed and easily discoverable in scholarly databases and search engines.

The new_rors provided in this case are:

  • 02vjkv261 (Sorbonne Université)
  • 02feahw73 (Paris Brain Institute)
  • 02en5vm52 (Inserm)
  • 00dcv1019 (CNRS)
  • 00jjx8s55 (INRIA)
  • 000zhpw23 (APHP)

By incorporating these ROR IDs into the corrected affiliation string, the publication can be definitively linked to these institutions. This ensures that each institution receives proper credit for its contributions and that the research output is accurately represented in scholarly records. The correction process involves mapping the researchers to their respective affiliations using these ROR identifiers, ensuring that the affiliations are consistent and accurate. This meticulous approach enhances the integrity of the research data and facilitates more reliable analyses of institutional research performance.

H2: Corrected Affiliation String and Implications

Based on the information provided, the corrected affiliation string would involve restructuring the raw data and incorporating the ROR identifiers for each institution. While the exact format may vary depending on the database or system in which the data is being entered, the key principles remain the same: clarity, accuracy, and completeness. A possible corrected format is shown below, but may not be the definitive, perfect answer:

  • Sorbonne Université (ROR ID: 02vjkv261): G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D., M.N., I.A.
  • Paris Brain Institute (ROR ID: 02feahw73): G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D.
  • Inserm (ROR ID: 02en5vm52): G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D., M.N., I.A.
  • CNRS (ROR ID: 00dcv1019): G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D., M.N., I.A.
  • INRIA (ROR ID: 00jjx8s55): G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D.
  • APHP (ROR ID: 000zhpw23): G.C., C.D.-F., E.P., S.S., L.D., P.C., R.K., R.H., H.H., J.-C.L., M.-L.W., P.P., A.B., S.T.d.M., A.D.
  • CATI: C.F., M.C., J.-F.M.
  • CEA: C.F., M.C., J.-F.M.
  • Institut de la Vision: M.N., I.A.
  • Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts: M.N., I.A.
  • National Rare Disease Center REFERET: M.N., I.A.
  • INSERM-DGOS CIC 1423: M.N., I.A.

The implications of this correction are significant. By accurately representing the affiliations, the publication’s metadata becomes more reliable, leading to improved discoverability and citation rates. Institutions receive appropriate credit for their contributions, which is vital for performance evaluations and funding opportunities. Furthermore, the use of ROR identifiers ensures that the data is consistent across different databases, facilitating more accurate bibliometric analyses and research assessments. The correction also exemplifies best practices in data management, highlighting the importance of meticulous attention to detail in academic publishing. Ultimately, the corrected affiliation string enhances the integrity of the research record and promotes transparency in scholarly communication.

H2: Conclusion

In conclusion, correcting raw affiliation strings is a crucial aspect of maintaining the integrity and accuracy of scholarly data. The case of the Sorbonne Université publication highlights the complexities involved in disambiguating institutional affiliations and the importance of using standardized identifiers like ROR IDs. By systematically identifying institutions, standardizing names, and applying ROR identifiers, it is possible to create a corrected affiliation string that accurately reflects the contributions of each institution. This process not only benefits the specific publication in question but also contributes to the broader goal of improving data quality and facilitating more reliable research assessments. Accurate affiliation data enhances the discoverability of research, ensures proper attribution, and supports informed decision-making in research funding and policy. Therefore, the effort invested in correcting raw affiliation strings is essential for promoting transparency and accountability in the academic community.

Correcting affiliations is very important for institutions in question and also for the researchers in those institutions. In addition, corrected affiliations help to improve institutional rankings, funding opportunities, and overall reputation.