ACM Journal of Data and Information Quality (JDIQ) is a multi-disciplinary journal that attracts papers ranging from theoretical research to algorithmic solutions to empirical research to experiential evaluations. Its mission is to publish high impact articles contributing to the field of data and information quality (IQ). 

JDIQ accepts research conducted using a wide variety of methods ranging from positivists to interpretive methods, systems building descriptions, and database theory, as well as statistical analysis, mathematical modeling, quasi experimental methods, hermeneutics, action research, and case study. JDIQ accepts diverse research methods that are customary in different research backgrounds and traditions, both quantitative and qualitative.  Research papers also need to provide valuable and relevant implications for applying their findings and solutions in practice.

Given the diversity of disciplines and author interests, ACM JDIQ welcomes experience papers, typically submitted by a practitioner or industrial researcher, survey papers that provide a critical assessment of the state of the art on specific IQ topics while highlighting open research challenges, and short challenge papers that describe a major research challenge to the JDIQ community.

ACM JDIQ is published on a quarterly basis. It also organizes special issues as part of the journal volume offering. ACM JDIQ special issues draw together a range of contributions on a given theme, and are an important mechanism for presenting a focused collection of significant work from areas of high innovation and activity to the Data Quality community.

Starting from 2019,  JDIQ welcomes a new type of invited contributions, called on the horizon papers. These manuscripts are written by top researchers in the field of Data Quality. They aim to introduce emerging new topics, their challenging aspects and envisioned solutions. They can be submitted by invitation only. 

JDIQ welcomes high-quality research contributions from the following areas, but not limited to:

    Data quality assessment
        Data quality metrics and frameworks
        Data profiling
        Error and anomaly detection
        Synthetic data and data synthesis
        Data provenance
        Probabilistic, incomplete and uncertain data
        Big data quality
    Data cleaning
        Data preparation and wrangling
        Entity matching, entity resolution, record linkage
        Data fusion
        Schema and ontology matching
        Data repair, augmentation, and imputation
        Data re-purposing
    Data quality and AI
        Data mining
        Data quality and machine learning
        Data quality and generative AI
        Data quality and representation learning
        Intelligent systems
        Automated planning and reasoning
        Federated learning
        Data poisoning
    Data governance
        Data quality standardization
        Responsible AI, bias and fairness
        Data quality policies and standards
        Legal aspects of data quality
        Maturity models
        Human-computer interaction
        Behavioral aspects of data quality
        Data privacy and access control
        Security evaluation
        Economics of data quality
        IQ education and curriculum development
    Data integration
        Data ecosystems
        Data acquisition
        Metadata management
        Web data management
        Semantic web and ontologies
        Information extraction
        Information foraging
        Multimodal data integration
        Knowledge graphs
        Knowledge representation and reasoning
    Application-specific data quality management
        Laboratory experimentation
        Healthcare and clinical data
        Financial data
        Social media data
        Multimedia and unstructured data
        Sensors and streaming data, IoT
        Robotic process automation
        Business process management
        Process mining