Working with data, whether networks of individual correspondents, tables of historical financial transactions, literary references, or results from archaeological surveys, can often require interpretation visually. There are a number of toolkits that can deal with specific or general types of data, and these can uncover patterns in your data that would be difficult to infer without digital tools.
D3: Data-Driven Documents
When working with data, it is almost always necessary to spend a good portion of time cleaning and standardizing that data for analysis or presentation. This is especially true when re-purposing data created by others (always ensure you have permission and give due credit). Useful resources for this work include:
There are still many tasks that cannot easily be performed by computers, and especially in the Humanities, the creation of usable research data often involves human intervention, even if on a basic level. Crowdsourcing can often create or enhance research data by asking participants to make judgements about the properties of artefacts, by deciphering handwriting or by interpreting basic information.
The most popular crowdsourcing platform is Zooniverse, which has a variety of projects that you can get directly involved and engaged with, from transcribing ships' logs to understand old weather, to deciphering ancient manuscripts and military field reports.
Geospatial technologies, which include application-based GIS systems, webmapping, and spatial network analysis, are used to define spatial relationships, both in relative terms and in with regards to their absolute position on the planet's surface.
In addition to these, the Digital Humanities considers such approaches as they apply to humanistic questions - how can they be used with incomplete, uncertain, contested and conflicting data? How might qualitative attributes such as emotional or political sentiment be captured? And how do we present results in a manner that conveys our conclusions, without eliminating important nuances, to an audience that may be unfamiliar with them?
Data management is the development of processes and procedures to suit a project's, or an organisation’s data requirements; processes and procedures are supported by an infrastructure, to protect and organise information assets. The concept emerged in the 1980s following the move from sequential processing to random access processing. Data management encompasses the detailed consideration of the following areas: database systems, masterdata and metadata management, quality control, integration definition, warehousing, transformation, governance and architecture. A Data Management Framework (DMF) is a system of thinking which allows a user of the Framework to correctly view data related concepts, and such frameworks are often applied in data management.