Data Collection
Overview
The data is politicians speaking to eachother about matters of concern of the Irish government.
https://www.oireachtas.ie/en/debates/
Dáil (House of Representatives)
- 174 TDs, Lower house, elected by public, most power.
Seanad (Senate)
- Upper house.
- Can debate, amend but not stop Dáil legislation.
- The 60 members are chosen by panels of industry specific politicians (43), the Taoiseach (11) and university graduates (6).
Committee
- Task groups of TDs and senators focused on specific topics (e.g. healthcare).
PQs (Parliamentary Questions)
- Questions submitted and directed to specific people for answers.
- Oral/Written but majority are written.
- Oral PQs overlap with Dáil debates
- Can filter written using URL/API to only select written PQs to avoid Dáil debate duplication.
Timeline
Data availibility
Dáil
- 1919 to Present
Seanad
- 1929 to Present
Committee
- 1924 to present
PQs
- Jan 2025? to present
XML -> CSV (AI Generated Description)
This document describes how the Akoma Ntoso XML from the Oireachtas is transformed into the flat CSV debates_all.csv
, and how to interpret each column—especially the text
field which holds the raw debate content.
- XML → CSV Mapping
Top‐level
<debate type="…">
Each of the four debate types (Dáil, Seanad, Committee, Questions) becomes one set of rows.Document‐level metadata
Extracted from<preface>
and<FRBRWork>
:doc_id
←/identification/FRBRWork/FRBRthis/@value
date
←<debate date="…">
title_ga
,title_en
←<preface>/<block name="title_ga|en">/<docTitle>
proponent_ga
,proponent_en
←<preface>/<block name="proponent_ga|en">/<docProponent>
status_ga
,status_en
←<preface>/<block name="status_ga|en">/<docStatus>
document_date
←<preface>/<block name="date_en">/<docDate>@date
volume
,number
←<preface>/<docNumber>@refersTo="#vol_…|#no_…"
Per‐element rows
Inside each<debateBody>
, elements are normalized to rows:element_type = summary
One row per<summary>
: prelude notes, suspension, interruptions.element_type = speech
One row per paragraph (<p>
) in a<speech>
: includes speaker id/name/role and timestamp.element_type = attendance
(committees only)
One row per committee sitting: theattendance
column holds a semicolon‐joined list of all<person>
names from the<rollCall>
.element_type = question
(questions only)
One row per<question>
: with separatequestion
andwritten_answer
columns and a combinedtext
field.
- CSV Columns Overview
• Common metadata:
doc_id
, source_type
, date
, title_ga
, title_en
,
proponent_ga
, proponent_en
, status_ga
, status_en
,
document_date
, volume
, number
, committee_name
,
question_type
, question_number
.
• Structural context:
section_name
, section_id
, heading_text
, heading_time
.
• Element descriptors:
element_type
, element_id
.
• Speaker or person:
speaker_id
, speaker_name
, speaker_role
, attendance
.
• Timing:
recorded_time
.
• Content:
- text
← Raw text of this row’s element.
- question
← The question text (only when element_type=question
).
- written_answer
← The written answer text (only when element_type=question
).
• Topic (questions only)
The to="…”
attribute from <question>
.
- Understanding the
text
Field
The text
column is your primary access to the debate content:
- For summaries: shows the summary line exactly as in the XML.
- For speeches: shows the full paragraph as spoken by a Deputy or Minister.
- For attendance: repeats the roll‑call header (e.g. “Members present:”).
- For
question
rows: concatenates the question and the written answer, so you can search Q&A in one go.
If you need to analyze just the spoken words, filter to element_type="speech"
and read text
.
For committee attendance, filter to element_type="attendance"
and parse the semicolon‐separated attendance
list.
For written questions/answers, filter to element_type="question"
and use question
vs. written_answer
or the combined text
.
- Example Queries
- All speeches by a given Deputy:
SELECT text FROM debates_all WHERE element_type = 'speech' AND speaker_name = 'Deputy Mary Lou McDonald';
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