There is global attention on new data analytic methods. Artificial Intelligence (AI) is seen as a critical technology, often relying on machine learning (where an algorithm is trained on data to recognise and predict patterns). Data scraping, the acquiring and structuring of information from online sources, is a typical first step for machine learning. The technologies of scraping, mining and learning are often conflated, as are the legal regimes under which they are regulated. One regulatory lever under one legal regime will not deliver policy aims, such as innovation, personal dignity, Open Science, or the currently popular ‘data sovereignty’. The legal issues involved in the governance of data range from proprietary approaches (copyright, database rights) to privacy and data protection. In addition, there is a wide range of public law instruments, for example relating to public sector data governance1, access to and use of user-facilitated data2 or the right to non-discrimination.3 Competition law again (which may be both privately and publicly enforceable) increasingly prescribes conduct in relation to data, such as in merger or acquisition cases, or in transparency provisions (Art. 17 CDSM; and centrally in the proposed DMA and AI Regulation). The scope of our enquiry in this report is within private law, specifically on the attempt to assert quasi-proprietary control of information and data, or vice versa limit such attempts, for example by exempting desired activities via copyright exceptions, such as the exception for text and data mining in Arts. 3 and 4 CDSM. Note: Please see the Word Doc upload of this report for a screen-reader-accessible version. If you experience other accessibility issues with our work, please contact h2020recreatingeurope@gmail.com. :