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Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.

The paper presents a brief history of the development of operations management (OM). This provides the backdrop for a content analysis of journal articles published in the Journal of Operations Management and the International Journal of Operations & Production Management between January 1990 and June 2003. MBA student survey data are then used to explore any gaps that may exist between the focus of academic research and the perceived importance of given OM subject areas to practitioners. The practical and conceptual insights highlighted are then used as the basis for a discussion of extant research priorities. The paper concludes with a preliminary conceptual framework that distinguishes between OM research seeking to consolidate operations practice and that which seeks to apply theoretical concepts into a practical context.

Social transfer programmes in developing countries are designed to contribute to poverty reduction by increasing the income of the poor in order to ensure minimal living standards. In addition, social transfers provide a safety net for the vulnerable, who are typically not covered by contributory social security. The question of how effective such programmes are in achieving these aims has been the subject of numerous impact evaluations. However, the optimal design of such programmes is still unclear. Even less is known about whether the adoption and implementation of transfer programmes is really driven by poverty and neediness or whether other factors also have an influence. To investigate these and other research questions, we have developed a new data set entitled Non-Contributory Social Transfer Programmes (NSTP) in Developing Countries. One advantage of this data set is that it traces 186 non-contributory programmes from 101 countries back in time and presents them in panel form for the period up until 2015. The second advantage is that it contains all the details regarding the various programmes' designs as well as information on costs and coverage in a coded format and thus facilitates both comparative quantitative and in-depth qualitative analyses. While describing the data we discuss a number of examples of how the data set can be used to explore different issues related to social policies in developing countries. We present suggestive evidence that the adoption of social transfer programmes is not based only on pro-poor motives, but rather that social policy choices differ between political regimes.

Development research techniques -- Social research : genesis and scope -- Research process -- Sampling and sample size estimation -- Data analysis -- Multivariate analysis -- Data analysis using quantitative software -- Population, health and nutrition -- Education -- Water and sanitation -- Poverty, inequality and rural development -- Environment and natural resource management

Introduction
Better use of administrative data is essential to enhance understanding about the family justice system, and characteristics and outcomes for children and families. The Nuffield Family Justice Observatory Data Partnership supports this aim through analyses of core family justice datasets. When a child is involved in family court proceedings in Wales, Cafcass Cymru are employed to represent a child's best interests. This paper provides an overview of the Cafcass Cymru data, and linkage to population level health and other administrative datasets held within the Secure Anonymised Information Linkage (SAIL) Databank. Two data linkage example analyses are described. Further research opportunities are outlined.
Methods
Cafcass Cymru data was transferred to SAIL using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. Three cohorts were created: all individuals involved in family court applications; all individuals with an ALF allowing subsequent health data linkage; and all individuals with a Residential Anonymised Linking Field (RALF) and Lower Super Output Area (LSOA) enabling area level deprivation analysis.
Results
Cafcass Cymru data are available containing 12,745 public law applications between 2011 and 2019, with 52,023 applications from 2005 to 2019 for private law. The overall match rate was 80%, with variations observed by time, law type, roles, gender and age. Forty per cent had hospital inpatient admissions 2 years prior or after application receipt at Cafcass Cymru, of which 27% were for emergency admissions; 54% had an emergency department attendance and 61% an outpatient appointment during the same period. Individuals involved in public or private law applications were more likely to reside in deprived areas.
Conclusion
The Nuffield Family Justice Observatory Data Partnership will enhance research opportunities to better understand the family justice system and outcomes for children and families. Population level Cafcass Cymru data can be accessed through the SAIL Databank. Forthcoming data acquisition will also facilitate further analyses and insight.