---
# System prepended metadata

title: 'DSLib WG: 2022 Planning'
tags: [liber-dslib]

---

---
tags: liber-dslib
---

# DSLib WG: 2022 Planning

## PLANNING

### Goals for 2022

| Date | Goal | 
| -------- | -------- | 
| May 12| Library profiles completed by DSLib wg members |  
| July 6 | Analysis of the ‘first batch’ of library profiles presented at Liber 2022 |
| End of 2022 | Landscape analysis report read for circulation |
| End of 2022 | Zotero library full of interesting sources of ideas |
| along the way | Exciting ideas and practices discussed in the meetings : ) |

**[!]** Library profiles are completed in 2 stages. First, by DSLib wg members. Second, by Liber members and network of the DSLib wg members. This allows to revise the template before it is circulated in a broader audience. 

**[!]** Landscape analysis = summary of the analysis of library profiles + summary from literature review.

**[!]** Literature review = update of the wg Zotero library and a review of literature

### Planning Until LIBER 2022

| Date | Goal | 
| -------- | -------- | 
| March 29 | Discuss (and finalise) the working definition of data science **AND** Organise ‘subgroups’: collections as data, library support, research support, research intelligence |
| April 5 | Task for each subgroup: clarify the current descriptions of scope of the data science activities within the subgroup by April 5 |
| April 10 | Survey finalised and can be filled in by the group |
| May 12 | Survey completed by the wg members|
| May 12-31 | Discuss the survey results in subgroups
| May 31 | Discuss the survey results and the workshop |
| June 29 | Finalise the analysis and the workshop agenda |
| July 6 | DSLib WG workshop |

### To-Do after LIBER 2022

1. Revise library profile templates and launch the survey
2. Start literature review (can already be started earlier!):
    - literature search + additions to the Zotero library
    - prep questions to be answered in lit review
    - collect examples of existing data science initiatives in libraries
    - collect examples of data science definitions
3. Analyse library profiles + write up sections for landscape analysis
4. Analyse data science definitions and prepare a wg infographic/infosheet of the wg proposed definitions of data science in libraries.

## CONTENT TO REVIEW

### (Working) definition of data science

Data science is a set of computational methods for the identification of novel and actionable insights from data. Computational methods used in data science include, but are not limited to, descriptive and inferential statistics, visualisation, text mining, image processing and computer vision, machine learning, and data engineering.

Data science in libraries is the use of data science methods in the delivery and/or improvement of library services.

Based on:
Kelleher, John D, and Brendan Tierney. Data Science. MIT Press, 2018.
Kotu, Vijay, and Balachandre Deshpande. Data Science: Concepts and Practice. Morgan Kaufmann, 2019.

### Current ‘raw’ descriptions of subgroups

#### Collections as Data
First, library collections can be framed as data (Padilla et al. 2019). This includes bibliographic descriptions, full-texts, images and other artefacts which are digitally available as part of library collections. 

#### Library support
Second, library systems supporting library functionality typically offer an abundance of activity data (e.g. loan data, usage of online resources, gate count).
Research support

#### Research Support

Third, there are research data, the storage and curation of which is increasingly taken up by research libraries.

#### Research intelligence

Research intelligence (RI), like business intelligence, regards compiling and visualizing data for decisions and benchmarking within the research community. Given the scale of the data available, RI often requires the implementation of data pipelines and dashboard tools. Examples of data collected are metadata of publications and other research outputs, and data related to these outputs such as citations. An integral part of RI is also the continuous development of analysis workflows, for instance combining traditional citation metrics with alternative metrics such as policy citations.

! Each of these groups require 2-3 sentences that specify the meaning and gives some concrete examples of data science activities. The current descriptions can be ignored! : )

## Extra

### Meeting format change

30 min to discuss the ongoing activities
20 min for ‘Member spotlight’. An opportunity for wg members to share/present/trigger discussions about data science activities at ‘home institution’.
10 min: …

! Meeting note format changes to a short summary written by ? after the meeting. Everyone still welcome to add any notes in the collaborative notebook.

Slack group for quicker communication?