{"id":4146,"date":"2026-01-28T16:59:00","date_gmt":"2026-01-28T16:59:00","guid":{"rendered":"https:\/\/fdm-nds.de\/?page_id=4146"},"modified":"2026-02-02T11:11:52","modified_gmt":"2026-02-02T11:11:52","slug":"step-3-processing-and-analyzing-data","status":"publish","type":"page","link":"https:\/\/fdm-nds.de\/index.php\/en\/step-3-processing-and-analyzing-data\/","title":{"rendered":"Step 3: Processing and Analyzing Data"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”Step 3: Processing and Analyzing Data” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”||28px|||” global_colors_info=”{}”]<\/p>\n

Introduction<\/h2>\n

For effective data analysis in research, the following aspects should be taken into account:<\/p>\n

    \n
  1. Data Checking, Validation, and Cleaning:<\/strong> Careful quality assurance is considered fundamental. Data should be checked and validated to identify and correct errors. Consistent cleaning is seen as ensuring that analyses are based on accurate and reliable data.<\/li>\n
  2. Use of Discipline-Specific Standards:<\/strong> In the data analysis phase, it is important to adhere to recognized and discipline-specific standards and methods of the field and to document them precisely (see excursus: Data documentation<\/strong><\/a>), particularly with regard to evaluation methods, controlled vocabularies, and file formats. Compliance with the principles of good scientific practice (GSP<\/strong><\/a>) is essential, especially guideline 11 [1], which emphasizes the application of scientifically sound and transparent methods, as well as quality assurance and the establishment of standards.<\/li>\n
  3. Preparation of Data for Scientific Publication:<\/strong> Data should be carefully prepared for scientific publication. This includes, for example, the anonymization of personal data and ensuring that all relevant information is understandable to the readership.<\/li>\n
  4. Documentation of Data Analysis:<\/strong> Complete documentation of all steps in the data analysis process is essential for understanding and reproducing the research (see excursus: Data documentation<\/a>). Researchers must record all relevant information leading to a research result in a manner that is traceable according to subject-specific requirements, so that verification and evaluation of the results are possible [2].<\/li>\n<\/ol>\n

    Legal Aspects & Ethics<\/h2>\n

    In the phase of data processing and analysis, careful consideration of legal aspects is required, especially those laid out in the General Data Protection Regulation (GDPR). A central issue concerns the processing of personal data (see excursus: legal aspects in RDM<\/strong><\/a>). The principle applies that personal data may only be processed if effective anonymization is possible and if a legal basis pursuant to Art. 6(1) GDPR [3] exists, either through the consent of the data subjects or a legal permission.
    When analyzing data with special requirements, such as dual use, protected species, animal testing, or medical data, the various legal and ethical aspects must be considered. Furthermore, it should be critically examined whether the research objectives could also be achieved with a smaller amount of data or through the use of anonymized or pseudonymized data, to ensure compliance with the data minimization principles of data protection law.<\/p>\n

    [\/et_pb_text][et_pb_divider _builder_version=”4.27.4″ _module_preset=”default” width=”33%” min_height=”18.8px” custom_margin=”-22px||0px|||” custom_padding=”13px||0px|||” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” min_height=”84.4px” custom_margin=”4px|||||” custom_padding=”0px||23px|||” global_colors_info=”{}”]<\/p>\n

    [1] https:\/\/wissenschaftliche-integritaet.de\/kodex<\/a>\u00a0
    [2]     https:\/\/wissenschaftliche-integritaet.de\/kodex\/dokumentation<\/a>\u00a0
    [3]     https:\/\/dsgvo-gesetz.de\/art-6-dsgvo<\/a>\u00a0<\/p>\n

    [\/et_pb_text][et_pb_heading title=”Further Information” _builder_version=”4.27.4″ _module_preset=”default” title_level=”h3″ global_colors_info=”{}”][\/et_pb_heading][et_pb_accordion _builder_version=”4.27.4″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][et_pb_accordion_item title=”Digital research data management (RDM) assistant for engineering research (currently under development): Javres” open=”on” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

    https:\/\/jarves.nfdi4ing.de\/<\/a><\/p>\n

    [\/et_pb_accordion_item][et_pb_accordion_item title=”Tool for anonymizing qualitative research data: QualiAnon” _builder_version=”4.27.4″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” open=”off” sticky_enabled=”0″]<\/p>\n

    Tool for anonymizing qualitative research data: <\/span>QualiAnon<\/a><\/p>\n

    Guidlines provided by QualiService: Link\u00a0<\/a><\/p>\n

    Video Tutorial for using QualiAnon provided by Qualiservice: Link<\/a><\/p>\n

    [\/et_pb_accordion_item][et_pb_accordion_item title=”Tool f\u00fcr statistical analytics: R” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n

    R is a free software environment for statistical computing and graphics: https:\/\/www.r-project.org<\/a><\/p>\n

    [\/et_pb_accordion_item][et_pb_accordion_item title=”Tool for cleaning and transforming data: OpenRefine” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n

    https:\/\/openrefine.org<\/a><\/p>\n

    A highly comprehensive introduction to the tool is offered by<\/span> SODa<\/a>.<\/p>\n

    [\/et_pb_accordion_item][et_pb_accordion_item title=”Tool for data anonymization across all disciplines: Amnesia” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n

    https:\/\/amnesia.openaire.eu<\/a><\/p>\n

    [\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

    Introduction For effective data analysis in research, the following aspects should be taken into account: Data Checking, Validation, and Cleaning: Careful quality assurance is considered fundamental. Data should be checked and validated to identify and correct errors. Consistent cleaning is seen as ensuring that analyses are based on accurate and reliable data. Use of Discipline-Specific […]<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-4146","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/pages\/4146","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/comments?post=4146"}],"version-history":[{"count":9,"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/pages\/4146\/revisions"}],"predecessor-version":[{"id":4423,"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/pages\/4146\/revisions\/4423"}],"wp:attachment":[{"href":"https:\/\/fdm-nds.de\/index.php\/wp-json\/wp\/v2\/media?parent=4146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}