# **V Data**

The **data** section of the metadata schema describes the actual data available to users, arranged in one or more datasets. For data with restricted access, access modalities can be described in the External Resources section. Data that are not accessible to anyone but the original researchers should not be described.

More than one _dataset_ may be associated with a given RCT. For example, the contributors may have collected census or administrative data for a larger sample than the ultimate study population, or specific information on two different populations affected by the RCT (e.g. buyers and sellers of a good). Datasets are distinct from data files; a dataset may be broken up into several files and even stored in multiple locations, for instance restricted-access GPS data vs. publicly accessible de-identified survey responses.<sup>12</sup> In general, a set of records may constitute its own dataset if it contains information central to the study, such as a separate outcome measure or the sampling frame, and (i) consists of observational units from a distinct study population (or sample from the study population) or (ii) is based on an independent data source (e.g. with a specific mode of data collection).

What delineates a dataset is ultimately up to the metadata contributor, but we recommend keeping the number of datasets to the minimum needed to describe the data well for users; often the data associated with a given RCT can be characterized as a single dataset. For example, the same dataset may contain several rounds of data collection, and the metadata schema allows multiple “cycles” (including repeated cross-sections). Even data from different sources can often be treated as part of the same dataset.

In some cases it can be useful to define a separate dataset to describe the data in sufficient detail. For example, two measures of the same outcome, such as measures of crop productivity obtained through inperson audits and satellite imagery, may have large discrepancies in coverage and diverging numbers of observations. In cluster-randomized studies, data may be available both at the cluster and individual levels, and these datasets should be described separately if each contains primary outcome measures. Similarly, a full census in the study area followed by sampling a subset of the population for the interventions and endline data collection warrants defining separate datasets for each data collection round.

For each dataset, contributors are asked to define a short description or _name_ , then provide information on the types and number of _observational units_ , first in total and then, further below, per arm. This information is collected at the dataset level in order to allow for multiple units of observation, for example outcomes measured at the teacher and the student levels. The per-arm fields are optional to allow description of data collected prior to treatment assignment (e.g. a study population census). A unit counts as a _targeted_ observation unit if it was selected or intended for data collection. This may be an estimated number. A

> 12Conversely, a data file may contain several datasets (e.g. an excel file with several sheets).

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