(Harvested on 1 January 2021)
This notebook attempts some large-scale analysis of files from the National Archives of Australia's RecordSearch database that have the access status of 'closed'. For a previous attempt at this, see Closed Access. For more background, see my Inside Story article from 2018.
See this notebook for the code used to harvest the data and create the CSV dataset.
import datetime
import altair as alt
import pandas as pd
The harvested data has been saved as a CSV file. First we'll open it up using Pandas.
df2020 = pd.read_csv(
"data/closed-20210101.csv",
parse_dates=["contents_start_date", "contents_end_date", "access_decision_date"],
keep_default_na=False,
)
df2020.head()
identifier | series | control_symbol | title | contents_date_str | contents_start_date | contents_end_date | location | access_status | access_decision_date_str | access_decision_date | reasons | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 65454 | A373 | 5117 | [Home Office suspect index] | 1946 - 1950 | 1946-01-01 | 1950-01-01 | Canberra | Closed | 26 Nov 1980 | 1980-11-26 | 33(1)(b) |
1 | 66910 | A432 | 1929/394 ATTACHMENT 2 | Abrahams. Opinions. | 1927 - 1928 | 1927-01-01 | 1928-01-01 | Canberra | Closed | 30 Jul 2018 | 2018-07-30 | 33(3)(a)(i)|33(3)(b) |
2 | 66911 | A432 | 1929/394 ATTACHMENT 3 | Legal opinions expressed by O Dixon, E Gorman ... | 1928 - 1928 | 1928-01-01 | 1928-01-01 | Canberra | Closed | 30 Jul 2018 | 2018-07-30 | 33(3)(a)(i)|33(3)(b) |
3 | 99746 | A471 | 49941 | [THOMAS Leslie Hector (Leading Aircraftman) : ... | 1943 - 1943 | 1943-01-01 | 1943-01-01 | Canberra | Closed | 20 May 1999 | 1999-05-20 | 33(1)(g) |
4 | 103094 | A518 | FJ118/6 | Nauru Census 1952 | 1952 - 1953 | 1952-01-01 | 1953-01-01 | Canberra | Closed | 24 Oct 1989 | 1989-10-24 | 33(1)(d)|33(1)(g) |
How many closed files are there?
df2020.shape[0]
11140
First let's see how many different series are represented in the data set.
df2020["series"].unique().shape[0]
686
Now let's look at the 25 most common series.
df2020["series"].value_counts()[:25]
K60 1671 A1838 1571 A13147 581 A6122 322 AWM54 309 A1209 293 A9737 232 B26 196 A1533 173 D4082 162 B73 158 A6135 154 E72 151 F1 126 AWM239 118 A432 114 PP946/1 112 A7452 103 A7324 85 C4384 76 C139 76 A3092 73 A2539 72 A1200 72 D1915 70 Name: series, dtype: int64
Series A1838 is familiar to anyone who's looked into the NAA's access examination process. It's a general correspondence series from DFAT, and requests for access tend to take a long time to be processed. Series K60 contains repatriation files from the Department of Veterans' Affairs, so these will often been withheld on privacy grounds. We'll see more about both of these below.
Let's chart the results.
# This creates a compact dataset to feed to Altair for charting
# We could make Altair do all the work, but that would embed a lot of data in the notebook.
# Save the series counts to a new dataframe.
series_counts = df2020["series"].value_counts().to_frame().reset_index()
series_counts.columns = ["series", "count"]
# Chart the results, sorted by number of files
alt.Chart(series_counts[:50]).mark_bar().encode(
x=alt.X("series", sort="-y"),
y=alt.Y("count", title="number of files"),
tooltip=["series", "count"],
)
This is only the top 50 of 686 series, so quite obviously there's a very long tail of series that have a small number of closed files.
Section 33 of the Archives Act defines a number of 'exemptions' – these are reasons why files should not be opened to public access. These reasons are recorded in RecordSearch, so we can explore why files have been closed. It's a little complicated, however, because multiple exemptions can be applied to a single file. The CSV data file records multiple reasons as a pipe-separated string. First we can look at the most common combinations of reasons.
df2020["reasons"].value_counts()[:25]
33(1)(g) 4573 Withheld pending adv 3398 Parliament Class A 1285 33(1)(a) 497 33(1)(a)|33(1)(b) 260 Closed period 214 33(1)(d)|33(1)(g) 149 33(1)(a)|33(1)(d)|33(1)(g) 120 Non Cwlth-no appeal 54 33(1)(a)|33(1)(b)|Withheld pending adv 53 33(1)(d) 50 49 33(1)(a)|Withheld pending adv 42 33(1)(a)|33(1)(d)|33(1)(e)(ii)|33(1)(g) 27 33(1)(e)(ii) 27 33(1)(a)|33(1)(d)|33(1)(e)(i)|33(1)(g) 25 33(1)(e)(ii)|33(1)(g) 25 33(3)(a)(i)|33(3)(b)|33(3)(a)(ii)|33(3)(b) 24 33(1)(d)|33(1)(e)(iii)|33(1)(g) 19 33(2)(a)|33(2)(b) 15 33(1)(b) 15 NRF 14 33(3)(a)(i)|33(3)(b) 13 33(1)(a)|33(1)(b)|33(1)(e)(iii) 9 Court records 9 Name: reasons, dtype: int64
It's probably more useful, however, to look at the frequency of individual reasons. So we'll split the pip-separated string and create a row for each file/reason combination.
df2020_reasons = df2020.copy()
# Split the reasons field on pipe symbol |. This turns the string into a list of values.
df2020_reasons["reason"] = df2020_reasons["reasons"].str.split("|")
# Now we'll explode the list into separate rows.
df2020_reasons = df2020_reasons.explode("reason")
Now we can look at the frequency of individual reasons. Not, of course, that the sum of the reasons will be greater than the number of files, as some files have multiple exemptions applied to them.
df2020_reasons["reason"].value_counts()
33(1)(g) 5003 Withheld pending adv 3524 Parliament Class A 1286 33(1)(a) 1096 33(1)(d) 429 33(1)(b) 362 Closed period 239 33(1)(e)(ii) 110 33(3)(b) 66 Non Cwlth-no appeal 60 49 33(1)(e)(iii) 46 33(3)(a)(i) 38 33(1)(e)(i) 37 33(1)(j) 30 33(3)(a)(ii) 28 33(2)(a) 24 33(2)(b) 24 NRF 15 MAKE YOUR SELECTION 12 Non Cwlth-depositor 10 Court records 9 33(1)(f)(i) 7 33(1)(f)(ii) 5 33(1)(f)(iii) 4 33(1)(h) 4 33(1)(c) 3 Destroyed 2 Name: reason, dtype: int64
The reasons starting with '33' are clauses in section 33 of the Archives Act. You can look up the Act to find out more about them, or look at this list on the NAA website. Some of the reasons, such as 'Parliament Class A' refer to particular types of records that are not subject to the same public access arrangements as other government records. Others, such as 'MAKE YOUR SELECTION' seem to be products of the data entry system!
Looking at the other most common reasons:
You might also notice that there's a blank line in the list above. This is because some closed files have no reasons recorded in RecordSearch. We can check this.
missing_reasons = df2020.loc[df2020["reasons"] == ""]
missing_reasons.shape[0]
49
There are 46 closed files with no reason recorded. Here's a sample.
missing_reasons.head()
identifier | series | control_symbol | title | contents_date_str | contents_start_date | contents_end_date | location | access_status | access_decision_date_str | access_decision_date | reasons | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
612 | 546435 | A1838 | 919/13/4 PART 31 | France - Disarmament - Nuclear Weapons Testing | 1972 - 1972 | 1972-01-01 | 1972-01-01 | Canberra | Closed | 07 Oct 2016 | 2016-10-07 | |
661 | 548392 | A1838 | 3127/3/4 | South Korea - Labour | 1959 - 1968 | 1959-01-01 | 1968-01-01 | Canberra | Closed | 11 Dec 2014 | 2014-12-11 | |
862 | 567499 | A1838 | 563/2/16 PART 9 | Radio Australia - Technical - Foreign broadcas... | 1961 - 1961 | 1961-01-01 | 1961-01-01 | Canberra | Closed | 29 Aug 2012 | 2012-08-29 | |
1351 | 733600 | AWM239 | 178 | [RAN Medical Officers' journals] PENGUIN (1 Ap... | 1945 - 1945 | 1945-01-01 | 1945-01-01 | Australian War Memorial | Closed | 14 Apr 2003 | 2003-04-14 | |
1560 | 853102 | AWM103 | R478/1/147 | [Headquarters, 1st Australian Task Force (HQ 1... | 1970 - 1970 | 1970-01-01 | 1970-01-01 | Australian War Memorial | Closed | 22 Jun 2009 | 2009-06-22 |
Let's change the missing reasons to 'None recorded' to make it easier to see what's going on.
df2020_reasons["reason"].replace("", "None recorded", inplace=True)
Let's chart the frequency of the different reasons.
# Once again we'll create a compact dataset for charting
reason_counts = df2020_reasons["reason"].value_counts().to_frame().reset_index()
reason_counts.columns = ["reason", "count"]
# Make the Chart
alt.Chart(reason_counts).mark_bar().encode(
x="reason", y=alt.Y("count", title="number of files"), tooltip=["reason", "count"]
)
It would be interesting to bring together the analyses above and see how reasons are distributed across series. First we need to reshape our dataset to show combinations of series and reasons.
# Group files by series and reason, then count the number of combinations
series_reasons_counts = (
df2020_reasons.groupby(by=["series", "reason"]).size().reset_index()
)
# Rename columns
series_reasons_counts.columns = ["series", "reason", "count"]
Now we can chart the results. Once again we'll show the number of files in the 50 most common series, but this time we'll highlight the reasons using color.
alt.Chart(series_reasons_counts).transform_aggregate(
count="sum(count)",
groupby=["series", "reason"]
# Sort by number of files
).transform_window(
rank="rank(count)",
sort=[alt.SortField("count", order="descending")]
# Get the top 50
).transform_filter(
alt.datum.rank < 50
).mark_bar().encode(
x=alt.X("series", sort="-y"),
y=alt.Y("sum(count)", title="number of files", axis=alt.Axis(grid=False)),
color="reason",
tooltip=["series", "reason", "count"],
)
Now we can see that the distribution of reasons varies considerably across series.
You would think that the sensitivity of material in closed files diminishes over time. However, there's no automatic re-assessment or time limit on 'closed' files. They stay closed until someone asks for them to be re-examined. That means that some of these files can be quite old. How old? We can use the contents end date to explore this.
# Normalise contents end values as end of year
df2020["contents_end_year"] = df2020["contents_end_date"].apply(
lambda x: datetime.datetime(x.year, 12, 31)
)
date_counts = df2020["contents_end_year"].value_counts().to_frame().reset_index()
date_counts.columns = ["end_date", "count"]
alt.Chart(date_counts).mark_bar().encode(x="year(end_date):T", y="count").properties(
width=700
)
alt.Chart(date_counts.loc[date_counts["end_date"] > "1890-12-31"]).mark_bar().encode(
x="year(end_date):T", y="count", tooltip="year(end_date)"
).properties(width=700)
df2020["years_old"] = df2020["contents_end_year"].apply(
lambda x: round((datetime.datetime.now() - x).days / 365)
)
age_counts = df2020["years_old"].value_counts().to_frame().reset_index()
age_counts.columns = ["age", "count"]
alt.Chart(age_counts.loc[age_counts["age"] < 130]).mark_bar().encode(
x=alt.X("age:Q", title="age in years"),
y=alt.Y("count", title="number of files"),
tooltip=["age", "count"],
).properties(width=700)
df2020["years_old"].describe()
count 11140.000000 mean 51.081598 std 19.422067 min 6.000000 25% 36.000000 50% 49.000000 75% 65.000000 max 222.000000 Name: years_old, dtype: float64
df2020.loc[df2020["reasons"].str.contains("33(1)(a)", regex=False)][
"years_old"
].describe()
count 1096.000000 mean 63.380474 std 12.805262 min 22.000000 25% 58.000000 50% 66.000000 75% 73.000000 max 96.000000 Name: years_old, dtype: float64
df2020["years_old"].quantile([0.25, 0.5, 0.75]).to_list()
[36.0, 49.0, 65.0]
df2020["year"] = df2020["access_decision_date"].dt.year
year_counts = df2020["year"].value_counts().to_frame().reset_index()
year_counts.columns = ["year", "count"]
alt.Chart(year_counts).mark_bar().encode(x="year:O", y="count")
df331a = df2020.loc[df2020["reasons"].str.contains("33(1)(a)", regex=False)]
df331a.head()
identifier | series | control_symbol | title | contents_date_str | contents_start_date | contents_end_date | location | access_status | access_decision_date_str | access_decision_date | reasons | contents_end_year | years_old | year | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | 140757 | A1838 | 3034/2/2/2 PART 7 | Indonesia. Communism in Indonesia | 1960 - 1962 | 1960-01-01 | 1962-01-01 | Canberra | Closed | 14 May 2012 | 2012-05-14 | 33(1)(a)|33(1)(b)|Withheld pending adv | 1962-12-31 | 60 | 2012 |
7 | 170971 | A816 | 41/301/195 | Exchange of staff between Joint Intelligence B... | 1953 - 1958 | 1953-01-01 | 1958-01-01 | Canberra | Closed | 29 Apr 1991 | 1991-04-29 | 33(1)(a)|33(1)(b) | 1958-12-31 | 64 | 1991 |
8 | 171089 | A816 | 43/302/76 | Cryptographic Material for ASIO | 1951 - 1952 | 1951-01-01 | 1952-01-01 | Canberra | Closed | 11 Mar 1993 | 1993-03-11 | 33(1)(a)|33(1)(b) | 1952-12-31 | 70 | 1993 |
9 | 171129 | A816 | 44/301/219 | SEATO [South East Asia Treaty Organisation] Co... | 1957 - 1957 | 1957-01-01 | 1957-01-01 | Canberra | Closed | 01 Aug 1991 | 1991-08-01 | 33(1)(a)|33(1)(b) | 1957-12-31 | 65 | 1991 |
12 | 200166 | A1196 | 29/501/225 | Evasion of Customs Regulations - RAAF Station,... | 1944 - 1944 | 1944-01-01 | 1944-01-01 | Canberra | Closed | 09 Apr 1975 | 1975-04-09 | 33(1)(a)|33(1)(b) | 1944-12-31 | 78 | 1975 |
series_counts_331a = df331a["series"].value_counts().to_frame().reset_index()
series_counts_331a.columns = ["series", "count"]
alt.Chart(series_counts_331a[:50]).mark_bar().encode(
x=alt.X("series", sort="-y"),
y=alt.Y("count", title="number of files"),
tooltip=["series", "count"],
)
dfwh = df2020.loc[df2020["reasons"].str.contains("Withheld pending adv", regex=False)]
dfwh.head()
identifier | series | control_symbol | title | contents_date_str | contents_start_date | contents_end_date | location | access_status | access_decision_date_str | access_decision_date | reasons | contents_end_year | years_old | year | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | 140757 | A1838 | 3034/2/2/2 PART 7 | Indonesia. Communism in Indonesia | 1960 - 1962 | 1960-01-01 | 1962-01-01 | Canberra | Closed | 14 May 2012 | 2012-05-14 | 33(1)(a)|33(1)(b)|Withheld pending adv | 1962-12-31 | 60 | 2012 |
10 | 171205 | A816 | 48/301/131 | Inland tele-radio channels in Australia, Papua... | 1950 - 1953 | 1950-01-01 | 1953-01-01 | Canberra | Closed | 16 Aug 2018 | 2018-08-16 | Withheld pending adv | 1953-12-31 | 69 | 2018 |
11 | 199284 | A1196 | 2/501/295 | Provision of Capacity for the Manufacture of n... | 1952 - 1955 | 1952-01-01 | 1955-01-01 | Canberra | Closed | 04 Mar 2020 | 2020-03-04 | Withheld pending adv | 1955-12-31 | 67 | 2020 |
13 | 200647 | A1196 | 36/501/729 PART 2 | RAAF Component of the Strategic Reserve- Execu... | 1958 - 1958 | 1958-01-01 | 1958-01-01 | Canberra | Closed | 02 Nov 2016 | 2016-11-02 | Withheld pending adv | 1958-12-31 | 64 | 2016 |
14 | 200648 | A1196 | 36/501/729 PART 3 | RAAF Component Strategic Reserve. (Execution o... | 1958 - 1959 | 1958-01-01 | 1959-01-01 | Canberra | Closed | 02 Nov 2016 | 2016-11-02 | Withheld pending adv | 1959-12-31 | 63 | 2016 |
series_counts_wh = dfwh["series"].value_counts().to_frame().reset_index()
series_counts_wh.columns = ["series", "count"]
alt.Chart(series_counts_wh[:50]).mark_bar().encode(
x=alt.X("series", sort="-y"), y="count"
)
dfwh["wait_days"] = dfwh["access_decision_date"].apply(
lambda x: round((datetime.datetime.now() - x).days)
)
/tmp/ipykernel_596751/1245189278.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy dfwh["wait_days"] = dfwh["access_decision_date"].apply(
dfwh["wait_years"] = dfwh["access_decision_date"].apply(
lambda x: round((datetime.datetime.now() - x).days / 365)
)
/tmp/ipykernel_596751/1295627524.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy dfwh["wait_years"] = dfwh["access_decision_date"].apply(
dfwh["wait_years"].value_counts()
5 652 10 499 7 384 6 376 8 334 2 325 3 275 4 252 11 243 9 114 12 44 13 11 32 2 24 2 19 2 33 1 22 1 30 1 14 1 27 1 31 1 17 1 16 1 15 1 Name: wait_years, dtype: int64
dfwh.loc[dfwh["wait_years"] > 10]
identifier | series | control_symbol | title | contents_date_str | contents_start_date | contents_end_date | location | access_status | access_decision_date_str | access_decision_date | reasons | contents_end_year | years_old | year | wait_days | wait_years | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
42 | 271461 | A1838 | 69/1/3/7 PART 2 | USSR Relations with Australia Activities of Au... | 1963 - 1964 | 1963-01-01 | 1964-01-01 | Canberra | Closed | 01 Nov 2011 | 2011-11-01 | Withheld pending adv | 1964-12-31 | 58 | 2011 | 3913 | 11 |
53 | 273271 | A1838 | 162/11/52 PART 1 | Congo. Relations with Other Countries - USSR | 1960 - 1960 | 1960-01-01 | 1960-01-01 | Canberra | Closed | 29 Nov 2011 | 2011-11-29 | Withheld pending adv | 1960-12-31 | 62 | 2011 | 3885 | 11 |
54 | 273272 | A1838 | 162/11/73 PART 2 | Congo. Relations with Other Countries. Congo -... | 1960 - 1961 | 1960-01-01 | 1961-01-01 | Canberra | Closed | 29 Nov 2011 | 2011-11-29 | Withheld pending adv | 1961-12-31 | 61 | 2011 | 3885 | 11 |
60 | 277191 | A6980 | S250793 | Non-British European Migration from China Part 6 | 1968 - 1979 | 1968-01-01 | 1979-01-01 | Canberra | Closed | 04 Aug 2010 | 2010-08-04 | Withheld pending adv | 1979-12-31 | 43 | 2010 | 4367 | 12 |
74 | 302544 | A1209 | 1957/4254 | ANZUS Council meeting - Washington, November 1956 | 1956 - 1956 | 1956-01-01 | 1956-01-01 | Canberra | Closed | 06 Apr 2010 | 2010-04-06 | 33(1)(a)|33(1)(b)|Withheld pending adv | 1956-12-31 | 66 | 2010 | 4487 | 12 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
8941 | 30091158 | A12381 | 20/47/1 | RCIS - Prime Minister Mr Fraser 1976 | 1975 - 1976 | 1975-01-01 | 1976-01-01 | Canberra | Closed | 13 Apr 2010 | 2010-04-13 | Withheld pending adv | 1976-12-31 | 46 | 2010 | 4480 | 12 |
9059 | 30713375 | A9737 | 1991/766 PART 1 | French Polynesia - nuclear testing | 25 Jul 1978 - 29 May 1991 | 1978-07-25 | 1991-05-29 | Canberra | Closed | 16 Jan 2012 | 2012-01-16 | Withheld pending adv | 1991-12-31 | 31 | 2012 | 3837 | 11 |
9073 | 30714039 | A9737 | 1990/1314 PART 1 | French nuclear testing | 27 Feb 1973 - 15 Mar 1995 | 1973-02-27 | 1995-03-15 | Canberra | Closed | 15 Dec 2011 | 2011-12-15 | Withheld pending adv | 1995-12-31 | 27 | 2011 | 3869 | 11 |
9340 | 31162900 | A4626 | 26 | Department - Attorney-General's | 18 Jul 1975 - 14 Apr 1976 | 1975-07-18 | 1976-04-14 | Canberra | Closed | 29 Jun 2011 | 2011-06-29 | Withheld pending adv | 1976-12-31 | 46 | 2011 | 4038 | 11 |
9341 | 31162905 | A4626 | 31 | Department of Foreign Affairs | 24 Jun 1974 - 18 Feb 1976 | 1974-06-24 | 1976-02-18 | Canberra | Closed | 29 Jun 2011 | 2011-06-29 | Withheld pending adv | 1976-12-31 | 46 | 2011 | 4038 | 11 |
313 rows × 17 columns
dfwh["wait_years"].describe()
count 3524.000000 mean 6.547673 std 3.039012 min 2.000000 25% 5.000000 50% 6.000000 75% 9.000000 max 33.000000 Name: wait_years, dtype: float64
dfwhs = df2020.loc[df2020["reasons"] == "Withheld pending adv"]
dfwhs["wait_years"] = dfwhs["access_decision_date"].apply(
lambda x: round((datetime.datetime.now() - x).days / 365)
)
/tmp/ipykernel_596751/1357762301.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy dfwhs["wait_years"] = dfwhs["access_decision_date"].apply(
dfwhs["wait_years"].describe()
count 3398.000000 mean 6.429665 std 2.778587 min 2.000000 25% 5.000000 50% 6.000000 75% 8.000000 max 24.000000 Name: wait_years, dtype: float64
dfwhs.loc[dfwhs["wait_years"] > 10]
identifier | series | control_symbol | title | contents_date_str | contents_start_date | contents_end_date | location | access_status | access_decision_date_str | access_decision_date | reasons | contents_end_year | years_old | year | wait_years | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
42 | 271461 | A1838 | 69/1/3/7 PART 2 | USSR Relations with Australia Activities of Au... | 1963 - 1964 | 1963-01-01 | 1964-01-01 | Canberra | Closed | 01 Nov 2011 | 2011-11-01 | Withheld pending adv | 1964-12-31 | 58 | 2011 | 11 |
53 | 273271 | A1838 | 162/11/52 PART 1 | Congo. Relations with Other Countries - USSR | 1960 - 1960 | 1960-01-01 | 1960-01-01 | Canberra | Closed | 29 Nov 2011 | 2011-11-29 | Withheld pending adv | 1960-12-31 | 62 | 2011 | 11 |
54 | 273272 | A1838 | 162/11/73 PART 2 | Congo. Relations with Other Countries. Congo -... | 1960 - 1961 | 1960-01-01 | 1961-01-01 | Canberra | Closed | 29 Nov 2011 | 2011-11-29 | Withheld pending adv | 1961-12-31 | 61 | 2011 | 11 |
60 | 277191 | A6980 | S250793 | Non-British European Migration from China Part 6 | 1968 - 1979 | 1968-01-01 | 1979-01-01 | Canberra | Closed | 04 Aug 2010 | 2010-08-04 | Withheld pending adv | 1979-12-31 | 43 | 2010 | 12 |
622 | 546984 | A1838 | 938/17/7/1 PART 2 | United Nations - Law of the Sea - Pollution of... | 1971 - 1972 | 1971-01-01 | 1972-01-01 | Canberra | Closed | 21 Sep 2011 | 2011-09-21 | Withheld pending adv | 1972-12-31 | 50 | 2011 | 11 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
8941 | 30091158 | A12381 | 20/47/1 | RCIS - Prime Minister Mr Fraser 1976 | 1975 - 1976 | 1975-01-01 | 1976-01-01 | Canberra | Closed | 13 Apr 2010 | 2010-04-13 | Withheld pending adv | 1976-12-31 | 46 | 2010 | 12 |
9059 | 30713375 | A9737 | 1991/766 PART 1 | French Polynesia - nuclear testing | 25 Jul 1978 - 29 May 1991 | 1978-07-25 | 1991-05-29 | Canberra | Closed | 16 Jan 2012 | 2012-01-16 | Withheld pending adv | 1991-12-31 | 31 | 2012 | 11 |
9073 | 30714039 | A9737 | 1990/1314 PART 1 | French nuclear testing | 27 Feb 1973 - 15 Mar 1995 | 1973-02-27 | 1995-03-15 | Canberra | Closed | 15 Dec 2011 | 2011-12-15 | Withheld pending adv | 1995-12-31 | 27 | 2011 | 11 |
9340 | 31162900 | A4626 | 26 | Department - Attorney-General's | 18 Jul 1975 - 14 Apr 1976 | 1975-07-18 | 1976-04-14 | Canberra | Closed | 29 Jun 2011 | 2011-06-29 | Withheld pending adv | 1976-12-31 | 46 | 2011 | 11 |
9341 | 31162905 | A4626 | 31 | Department of Foreign Affairs | 24 Jun 1974 - 18 Feb 1976 | 1974-06-24 | 1976-02-18 | Canberra | Closed | 29 Jun 2011 | 2011-06-29 | Withheld pending adv | 1976-12-31 | 46 | 2011 | 11 |
282 rows × 16 columns