ready4 – Data

Blog: Framework taxonomies dataset releases

Wed, 28 Jan 2026 00:00:00 +0000

Releases of the datasets of the taxonomies that support a consistent code house-style and automation of documentation autoring are described below.

Date	Dataset	Version	Number of files
23-Dec-2022	ready4 Framework Abbreviations and Definitions	3.0	1
25-May-2022	ready4 Framework Abbreviations and Definitions	2.1	1
14-Jan-2022	ready4 Framework Abbreviations and Definitions	2.0	1
14-Jan-2022	ready4 Framework Abbreviations and Definitions	1.0	1

Blog: Dataset releases

Wed, 18 Jan 2023 00:00:00 +0000

All current and previous releases of framework datasets.

Docs: Finding and using open access data

Thu, 05 Jan 2017 00:00:00 +0000

Docs: Data

Thu, 05 Jan 2017 00:00:00 +0000

Docs: Ingest data from an open access repository

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a R Markdown program from the Acumen website. You can use the following links to:

view the tutorial on the Acument website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

1. Objectives

On completion of this tutorial you should be able to:

Understand basic concepts relating to the Australian Mental Health Systems Models Dataverse Collection; and
Have the ability to search for, download and ingest files contained in Dataverse Datasets that are linked to by the Australian Mental Health Systems Models Dataverse Collection using two alternative approaches;
- Using a web based interface; and
- Using R commands.

2. Prerequisites

You can complete most of this tutorial without any specialist skills or software other than having a web-browser connected to the Internet. However, if you wish to try running the R code for finding and downloading files described in the last part of the tutorial, then you must have R (and ideally RStudio as well) installed on your machine. Instructions for how to install this software are available at https://rstudio-education.github.io/hopr/starting.html .

3. Concepts

Before searching for or retrieving data from the Australian Mental Health Systems Models Dataverse Collection, the following concepts are useful to understand:

The Dataverse Project is “an open source web application to share, preserve, cite, explore, and analyze research data.” It is developed at Harvard’s Institute for Quantitative Social Science (IQSS). More information about the project is available on the Dataverse Project’s website.
There are many Dataverse Installations around the world (85 at the time of writing this tutorial). Each Dataverse Installation is an instance of an organisation installing the Dataverse Project’s software on its own servers to create and manage online data repositories. At the time of writing there is one Australian Dataverse Installation listed on the Dataverse Project’s website, which is the Australian Data Archive.
The Harvard Dataverse is a Dataverse Installation that is managed by Harvard University, that is open to researchers from all disciplines from anywhere in the world. More details are available from its website.
A Dataverse Collection (frequently and confusingly also referred to as simply a “Dataverse”) is a part of a Dataverse Installation that a user can set up to host multiple “Dataverse Datasets” (see next bullet point). Dataverse Collections typically share common attributes (for example, are in the same topic area or produced by the same group(s) of researchers) and can be branded to a limited degree. Dataverse Collections will also contain descriptive metadata about the purpose and ownership of the collection.
A Dataverse Dataset is a uniquely identified collection of files (some of which, again confusingly, can be tabular data files of the type that researchers typically refer to as “datasets”) within a Dataverse Collection. Each Dataverse Dataset will have a name, a Digital Object Identifier, a version number, citation information and details of the licensing/terms of use that apply to its contents.
A Linked Dataverse Dataset is a Dataverse Dataset that appears in a Dataverse Collection’s list of contents without actually being in that Dataverse Collection (it is hosted in another Dataverse Collection and is potentially owned and controlled by another user).
The Australian Mental Health Systems Models Dataverse Collection (which we will refer to as “our Dataverse Collection”) is a Dataverse Collection of Linked Dataverse Datasets within the Harvard Dataverse. We established our Dataverse Collection in the Harvard Dataverse because of the robustness and flexibility that this service provides. A factor in our choice of the Harvard Dataverse was that the aim of our Dataverse Collection is to promote easy access to non-confidential data relevant to modelling Australian mental health policy and service planning topics. The non-confidential nature of the data means that the additional administrative requirements that some other Dataverse Installations place on users were potentially unnecessary for our specific purposes. As a collection of Linked Dataverse Datasets, our Dataverse Collection can be used by modelling groups as both a centralised location to find relevant data and as an additional promotion / distribution channel to share Dataverse Datasets from their own Harvard Dataverse Collections without surrendering any control over the management of their data (they continue to curate their Dataverse Dataset and can modify Dataverse Dataset contents, metadata and terms of use as they see fit).

3. Search and download dataset files

There are multiple options for searching and downloading files contained in our Dataverse Collection. This tutorial will discuss just two - one based on using a web browser and the other based on using R commands. For details on other options, it is recommended to consult the Harvard Dataverse user guide and (for more technical readers) api guide.

3.1. Web browser approach

Searching and retriving data from our Dataverse Collection via a web-browser is very simple, and this methods is suitable for low volume requests (i.e. occasional use) where reproducibility is not important.

To find and download data using your web browser, implement the following steps:

Go to our Dataverse Collection at https://dataverse.harvard.edu/dataverse/openmind
Search for the Linked Dataverse Dataset most of interest to you by using the tools provided on the landing page.
Click on the link to your selected Dataverse Dataset. Note that by doing so you will leave our Dataverse Collection and be taken to the Dataverse Collection controlled by the Dataverse Dataset’s owner.
(Optional) - Click on the “Metadata”, “Terms” and “Versions” tabs or (if available) the Related Publication links to discover more about the dataset. When you are done, click on the “Files” tab to review the files contained in the Dataverse Dataset.
Select the files that you wish to download using the checkboxes and click on the “Download” button.
When prompted, review any terms of use you are presented with and either accept them or cancel the download as you feel appropriate.

More detail on some of the above steps is available in the following section of the Harvard Dataverse user guide: https://guides.dataverse.org/en/latest/user/find-use-data.html#finding-data

3.2 Using R commands

Some limitations of relying purely on a web-browser are that it is a purely manual approach that can become inefficient for large number of data requests and which is not reproducible (thereby limiting transparency about the specific data items / versions used in an analysis). It can therefore be desirable to explore alternatives that are based on programming commands. Programmatic approaches have the advantage of being more readily incorporated into automated and reproducible workflows.

There are a range of software tools in different languages that can be used to programmatically search and retrieve files in Dataverse Collections. More information on these resources on a dedicated page within the Dataverse Project’s documentation.

One of these tools is dataverse - “the R Client for Dataverse Repositories”. The dataverse R package has a range of functions that are very helpful for general tasks relating to the search and retrieval of files contained in Dataverse Datasets. These functions are not the focus of this tutorial, but you can read more about them on the [packages documentation website]((https://iqss.github.io/dataverse-client-r/).

The remainder of this tutorial is focused on the use of another R package called ready4use which created by Orygen to help manage open-source data for use in mental health models. The ready4use R package extends the dataverse R package and one of its applications is to ingest R objects stored in Dataverse Datasets in the “.Rds” file format directly into an R Session’s working memory. More information about ready4use is available on its documentation website.

3.2.1 Install and load required R packages

As ready4use is still a development package, you may need to first install the devtools package to help install it. The following commands entered in your R console will do this.

utils::install.packages("devtools")
devtools::install_github("ready4-dev/ready4use")

We now load the ready4use package and the ready4 framework for youth mental health modelling that it depends on. The ready4 framework will have been automatically installed along with ready4use.

library(ready4)
library(ready4use)

3.2.2 Specify repository details

The next step is to create a Ready4useRepos object, which in this example we will call X, that contains the details of the Dataverse Dataset from which we wish to retrieve R objects. We need to supply three pieces of information to Ready4useRepos. Two of these items of information will be the same for any data item retrieved from our Dataverse Collection and are the Dataverse Collection identifier (which for us is “openmind”) and the server on which the containing Dataverse Installation is hosted (in our case “dataverse.harvard.edu”). The one item of information that will vary based on your requirements is the name / identifier (DOI) of the Dataverse Dataset from which we wish to retrieve data. In this example we are using the DOI for the “Synthetic (fake) youth mental health datasets and data dictionaries” Dataverse Dataset.

X <- Ready4useRepos(dv_nm_1L_chr = "openmind",
                    dv_server_1L_chr = "dataverse.harvard.edu",
                    dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/HJXYKQ")

Having supplied the details of where the data is stored we can now ingest the data we are interested in. We can either ingest all R object in the selected Dataverse Dataset or just objects that we specify. By default R objects are ingested along with their metadata, but we can choose not to ingest the metadata.

3.2.3 Ingest all R objects from a Dataverse Dataset along with its metadata

To ingest all R objects in the dataset, we enter the following command.

Y <- ingest(X)

We can now create separate list objects for the ingested data and its metadata.

data_ls <- procureSlot(Y,"b_Ready4useIngest@objects_ls")
meta_ls <- procureSlot(Y,"a_Ready4usePointer@b_Ready4useRepos@dv_ds_metadata_ls$ds_ls")

We can itemise the data objects we have ingested with the following command.

names(data_ls)

#> [1] "eq5d_ds_dict"         "eq5d_ds_tb"           "ymh_clinical_dict_r3" "ymh_clinical_tb"      "ymh_phq_gad_tb"

We can also see what metadata fields we have ingested.

names(meta_ls)

#>  [1] "id"                           "datasetId"                    "datasetPersistentId"          "datasetType"                  "storageIdentifier"            "versionNumber"               
#>  [7] "internalVersionNumber"        "versionMinorNumber"           "versionState"                 "latestVersionPublishingState" "deaccessionLink"              "lastUpdateTime"              
#> [13] "releaseTime"                  "createTime"                   "publicationDate"              "citationDate"                 "termsOfUse"                   "fileAccessRequest"           
#> [19] "metadataBlocks"               "files"

There can be a lot of useful information contained in this metadata list object. For example, we can retrieve descriptive information about the Dataverse Dataset from which we have ingested data.

meta_ls$metadataBlocks$citation$fields$value[[7]]$dsDescriptionValue$value

#> [1] "The datasets in this collection are entirely fake. They were developed principally to demonstrate the workings of a number of utility scoring and mapping algorithms. However, they may be of more general use to others. In some limited cases, some of the included files could be used in exploratory simulation based analyses. However, you should read the metadata descriptors for each file to inform yourself of the validity and limitations of each fake dataset. To open the RDS format files included in this dataset, the R package ready4use needs to be installed (see https://ready4-dev.github.io/ready4use/ ). It is also recommended that you install the youthvars package ( https://ready4-dev.github.io/youthvars/) as this provides useful tools for inspecting and validating each dataset."

The metadata also contains descriptive information on each file in the Dataverse Dataset.

meta_ls$files$description[5]

#> [1] "A synthetic (fake) dataset representing clients in an Australian primary youth mental health service. This dataset was generated from parameter values derived from a sample of 1107 clients of headspace services using a script that is also included in this dataset. The purpose of this synthetic dataset was to allow the replication code for a utility mapping study (see: https://doi.org/10.1101/2021.07.07.21260129) to be run by those lacking access to the original dataset. The dataset may also have some limited value as an input dataset for purely exploratory studies in simulation studies of headspace clients, as its source dataset was reasonably representative of the headpace client population. However, it should be noted that the algorithm that generated this dataset only captures aspects of the joint distributions of the psychological and health utility measures. Other sample characteristic variables (age, gender, etc) are only representative of the source dataset when considered in isolation, rather than jointly."

3.2.4 Ingest all R objects from a Dataverse Dataset without metadata

If we wished to ingest only the R objects without metadata, we could have simply run the following command.

data_2_ls <- ingest(X,
                    metadata_1L_lgl = F)

We can see that this ingest is identical to that made using the previous method.

identical(data_ls, data_2_ls)

#> [1] TRUE

3.2.5 Ingest selected R objects

If we only want to ingest one specific object, we can supply its name.

ymh_clinical_tb <- ingest(X,
                          fls_to_ingest_chr = c("ymh_clinical_tb"),
                          metadata_1L_lgl = F)

The output from an object specific call to the ingest method is the requested object.

ymh_clinical_tb %>%
  head()

#> # A tibble: 6 × 43
#>   fkClientID    round   d_interview_date d_age d_gender d_sex_birth_s d_sexual_ori_s d_ATSI d_country_bir_s d_english_home d_english_native d_studying_working d_relation_s s_centre c_p_diag_s
#>   <chr>         <fct>   <date>           <int> <chr>    <chr>         <fct>          <chr>  <chr>           <chr>          <chr>            <chr>              <chr>        <chr>    <chr>     
#> 1 Participant_1 Baseli… 2020-03-22          14 Male     Male          Heterosexual   No     Australia       Yes            Yes              Not studying or w… In a relati… Southpo… Other     
#> 2 Participant_2 Baseli… 2020-06-15          19 Female   Female        Heterosexual   Yes    Other           No             No               Studying only      In a relati… Regiona… Anxiety   
#> 3 Participant_3 Baseli… 2020-08-20          21 Female   Female        Other          NA     NA              NA             NA               Studying only      Not in a re… Canberra Anxiety   
#> 4 Participant_4 Baseli… 2020-05-23          12 Female   Female        Heterosexual   Yes    Other           No             No               Not studying or w… In a relati… Southpo… Depressio…
#> 5 Participant_5 Baseli… 2020-04-05          19 Male     Male          Heterosexual   Yes    Other           No             No               Not studying or w… Not in a re… Southpo… Depressio…
#> 6 Participant_6 Baseli… 2020-06-09          19 Male     Male          Heterosexual   Yes    Other           No             No               Studying only      In a relati… Regiona… Anxiety   
#> # ℹ 28 more variables: c_clinical_staging_s <chr>, k6_total <int>, phq9_total <int>, bads_total <int>, gad7_total <int>, oasis_total <int>, scared_total <int>, c_sofas <int>,
#> #   aqol6d_q1 <int>, aqol6d_q2 <int>, aqol6d_q3 <int>, aqol6d_q4 <int>, aqol6d_q5 <int>, aqol6d_q6 <int>, aqol6d_q7 <int>, aqol6d_q8 <int>, aqol6d_q9 <int>, aqol6d_q10 <int>,
#> #   aqol6d_q11 <int>, aqol6d_q12 <int>, aqol6d_q13 <int>, aqol6d_q14 <int>, aqol6d_q15 <int>, aqol6d_q16 <int>, aqol6d_q17 <int>, aqol6d_q18 <int>, aqol6d_q19 <int>, aqol6d_q20 <int>

We can also request to ingest multiple specified objects from a Dataverse Dataset.

data_3_ls <- ingest(X,
                    fls_to_ingest_chr = c("ymh_clinical_tb","ymh_clinical_dict_r3"),
                    metadata_1L_lgl = F)

This last request produces a list of ingested objects.

names(data_3_ls)

#> [1] "ymh_clinical_dict_r3" "ymh_clinical_tb"

Docs: Search open access data collections

Thu, 30 Nov 2023 00:00:00 +0000

library(ready4)

The make_datasets_tb function from the ready4 library can be used to create a summary table of the open access datasets we curate in our ready4 Dataverse Collection.

make_datasets_tb("ready4") -> x

One way to inspect this information is to group contents by Dataverse Collections using the print-data function.

print_data(x,
           by_dv_1L_lgl = T) %>%
  kableExtra::scroll_box(width = "100%")

Dataverse	Name	Description	Creator	Datasets
TTU	Transfer to Utility	A collection of transfer to utility datasets developed with the ready4 open science framework.	Orygen	1, 2, 3
fakes	Fake Data For Instruction And Illustration	Fake data used to illustrate toolkits developed with the ready4 open science framework.	Orygen	4 , 5 , 6 , 7 , 8 , 9 , 10
firstbounce	First Bounce	A ready4 framework model of platforms. Aims to identify opportunities to improve the efficiency and equity of mental health services.	Orygen
ready4fw	ready4 Framework	A collection of datasets that support implementation of the ready4 framework for open science computational models of mental health systems.	Orygen	11
readyforwhatsnext	readyforwhatsnext	Data collections for the readyforwhatsnext mental health systems model.	Orygen	12, 13
springtides	Springtides	A ready4 framework model of places. Synthesises geometry (boundary, coordinate) and spatial attribute (e.g. population counts, environmental characteristics, service identifier and model coefficients associated with areas) data.	Orygen	14
springtolife	Spring To Life	A ready4 framework model of people. Models the characteristics, behaviours, relationships and outcomes of groups of individuals relevant to policymakers and service planners aiming to improve population mental health.	Orygen	15

Alternatively, we can itemise individual Dataverse Datasets. When doing so, it makes sense to prepare separate views for toy datasets designed for instruction and real datasets appropriate for use in modelling.

Datasets appropriate for use in modelling projects can be returned by supplying the value “real” to the what_1L_chr argument of print_data.

print_data(x,
           what_1L_chr = "real") %>%
  kableExtra::scroll_box(width = "100%")

Title	Description	Dataverse
Transfer to AQoL-6D Utility Mapping Algorithms	Catalogues of models (and the programs that produced them) that can be used in conjunction with the youthu R package to predict AQoL-6D health utility (and thus, derive QALYs) from measures collected in youth mental health services.	TTU
Transfer to AQoL-6D From Measures Collected In Primary Youth Mental Health Services	This is a work in progress dataset to support the implementation and reporting of a study to map measures collected in Australian primary youth mental health services to AQoL-6D health utility.	TTU
Transfer to CHU-9D From Measures Collected In Primary Youth Mental Health Services	This is a work in progress dataset to support the implementation and reporting of a study to map measures collected in Australian primary youth mental health services to CHU-9D health utility	TTU
ready4 Framework Abbreviations and Definitions	This dataset contains resources that help ready4 Framework Developers adopt common standards and workflows.	ready4fw
readyforwhatsnext posters	A collection of poster summaries about the readyforwhatsnext project and its outputs.	readyforwhatsnext
Australian demographic input parameters for Springtides model	Geometry, spatial attribute and metadata inputs for the demographic module of the readyforwhatsnext model. The demographic module is a systems dynamics spatial simulation of area demographic characteristics. The current version of the model is quite rudimentary and is designed to be extended by other models developped with the ready4 open science mental health modelling tools.	readyforwhatsnext
Springtides reports for Local Government Areas in the North West of Melbourne	This dataset is a collection of reports generated by a development version of the Springtides Model Of Places. Each report summarises prevalence projections for a specified mental disorder / mental health condition for a Local Government Area that is wholly or partially within the catchment area of the Orygen youth mental health service in North West Melbourne. As these reports were generated by a development version of the Springtides Model, these projections should be regarded as exploratory.	springtides
Modelling the online helpseeking choice of socially anxious young people	Models to predict the online helpseeking choices of socially anxious young people in Australia and replication code and documentation to implement the discrete choice experiment that generated the models. All study outputs were created with the aid of the mychoice R package (https://ready4-dev.github.io/mychoice).	springtolife

To view toy datasets, instead supply the value “fakes”.

print_data(x,
           what_1L_chr = "fakes") %>%
  kableExtra::scroll_box(width = "100%")

Title	Description	Dataverse
TTU (Transfer to Utility) R package - AQoL-6D vignette output	This dataset has been generated from fake data as an instructional aid. It is not to be used to inform decision making.	fakes
TTU (Transfer to Utility) R package - EQ-5D vignette output	This dataset is provided as a teaching aid. It is the output of tools from the TTU R package, applied to a synthetic dataset (Fake Data) of psychological distress and psychological wellbeing. It is not to be used to support decision-making.	fakes
Synthetic (fake) youth mental health datasets and data dictionaries	The datasets in this collection are entirely fake. They were developed principally to demonstrate the workings of a number of utility scoring and mapping algorithms. However, they may be of more general use to others. In some limited cases, some of the included files could be used in exploratory simulation based analyses. However, you should read the metadata descriptors for each file to inform yourself of the validity and limitations of each fake dataset. To open the RDS format files included in this dataset, the R package ready4use needs to be installed (see ). It is also recommended that you install the youthvars package ( ) as this provides useful tools for inspecting and validating each dataset.	fakes
ready4use R package vignette output	This dataset is provided so that others can compare the output they generate when implementing vignette code with that generated by the authors.	fakes
Specific R Package - AQoL-6D Vignette Output	This dataset is provided so that others can apply the algorithms we have developed, consistent with the principles of the ready4 open science framework for data synthesis and simulation in mental health.	fakes
Synthetic (fake) dataset for hypothetical replication of study mapping psychological distress and functioning measures to AQoL-6D health utility	This dataset is comprised of fake data that has been created to illustrate the potential transfer of a study algorithm for creating utility mapping models to new data. Outputs in this dataset are for instructional purposes only and should not be used to inform decision making.	fakes
Synthetic (fake) dataset for hypothetical replication of study mapping psychological distress and functioning measures to CHU-9D health utility	This dataset is comprised of fake data that has been created to illustrate the potential transfer of a study algorithm for creating CHU-9D utility mapping models to new data. Outputs in this dataset are for instructional purposes only and should not be used to inform decision making	fakes

Docs: Add a data dictionary to a dataset

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a vignette article from the ready4use library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

Note: This vignette is illustrated with fake data. The dataset explored in this example should not be used to inform decision-making.

library(ready4)
library(ready4use)

ready4use includes a number of tools for labeling health economic model data and forms part of the ready4 framework.

Create a dataset-dictionary pair

A data dictionary contains useful metadata about a dataset. To illustrate this point, we can ingest examples of a toy (fake) dataset and its data-dictionary.

objects_ls <- Ready4useRepos(dv_nm_1L_chr = "fakes",
                    dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/HJXYKQ",
                    dv_server_1L_chr = "dataverse.harvard.edu") %>%
  ingest(metadata_1L_lgl = F)

Importantly (and a requirement for subsequent steps), the data dictionary we ingest is a ready4use_dictionary object.

class(objects_ls$eq5d_ds_dict)
#> [1] "ready4use_dictionary" "ready4_dictionary"    "tbl_df"               "tbl"                  "data.frame"

We can now pair the data dictionary with its dataset in a new object X, a Ready4useDyad.

X <- Ready4useDyad(ds_tb = objects_ls$eq5d_ds_tb,
                   dictionary_r3 = objects_ls$eq5d_ds_dict)

Inspect data

We can inspect X by printing selected information about it to console using the exhibit method. If we only wish to see the first or last few records, we can pass “head” or “tail” to the display_1L_chr argument.

 exhibit(X,
         display_1L_chr = "head",
         scroll_box_args_ls = list(width = "100%"))

Dataset
uid	Timepoint	data_collection_dtm	d_age	Gender	d_sex_birth_s	d_sexual_ori_s	d_relation_s	d_ATSI	CALD	Region	d_studying_working	eq5dq_MO	eq5dq_SC	eq5dq_UA	eq5dq_PD	eq5dq_AD	K10_int	Psych_well_int
1	BL	2019-10-22	14	Male	Male	Heterosexual	In a relationship	No	No	Metro	Not studying or working	1	1	1	1	2	11	87
2	BL	2019-10-17	19	Female	Female	Heterosexual	In a relationship	Yes	Yes	Regional	Studying only	1	2	1	1	1	14	65
2	FUP	2020-02-14	19	Female	Female	Heterosexual	In a relationship	Yes	Yes	Regional	Studying only	3	1	1	1	1	10	71
3	BL	2020-02-15	21	Female	Female	Other	Not in a relationship	NA	NA	Metro	Studying only	1	1	3	1	1	13	74
3	FUP	2020-06-14	21	Female	Female	Other	Not in a relationship	NA	NA	Metro	Studying only	1	1	2	1	1	10	64
4	BL	2019-12-14	12	Female	Female	Heterosexual	In a relationship	Yes	Yes	Metro	Not studying or working	1	1	1	3	1	18	40

The dataset may be more meaningful if variables are labelled using the descriptive information from the data dictionary. This can be accomplished using the renew method.

X <- renew(X,
           type_1L_chr = "label")

exhibit(X,
        display_1L_chr = "head",
         scroll_box_args_ls = list(width = "100%"))

Dataset
Unique identifier	Data collection round	Date of data collection	Age	Gender (grouped)	Sex at birth	Sexual orientation	Relationship status	Aboriginal or Torres Strait Islander	Culturally And Linguistically Diverse	Region of residence (metropolitan or regional)	Education and employment status	EQ5D - Mobility domain score	EQ5D - Self-Care domain score	EQ5D - Usual Activities domain score	EQ5D - Pain / Discomfort domain score	EQ5D - Anxiety / Depression domain score	Kessler Psychological Distress - 10 Item Total Score	Overall Wellbeing Measure (Winefield et al. 2012)
1	BL	2019-10-22	14	Male	Male	Heterosexual	In a relationship	No	No	Metro	Not studying or working	1	1	1	1	2	11	87
2	BL	2019-10-17	19	Female	Female	Heterosexual	In a relationship	Yes	Yes	Regional	Studying only	1	2	1	1	1	14	65
2	FUP	2020-02-14	19	Female	Female	Heterosexual	In a relationship	Yes	Yes	Regional	Studying only	3	1	1	1	1	10	71
3	BL	2020-02-15	21	Female	Female	Other	Not in a relationship	NA	NA	Metro	Studying only	1	1	3	1	1	13	74
3	FUP	2020-06-14	21	Female	Female	Other	Not in a relationship	NA	NA	Metro	Studying only	1	1	2	1	1	10	64
4	BL	2019-12-14	12	Female	Female	Heterosexual	In a relationship	Yes	Yes	Metro	Not studying or working	1	1	1	3	1	18	40

To remove dataset labels, use the renew method with “unlabel” passed to the type_1L_chr argument.

X <- renew(X,
           type_1L_chr = "unlabel")

By default, the exhibit method will print the dataset part of the Ready4useDyad instance. To inspect the data dictionary, pass “dict” to the type_1L_chr argument.

exhibit(X,
        display_1L_chr = "head",
        type_1L_chr = "dict",
        scroll_box_args_ls = list(width = "100%"))

Data Dictionary
Variable	Category	Description	Class
CALD	demographic	Culturally And Linguistically Diverse	factor
d_age	demographic	age	integer
d_ATSI	demographic	Aboriginal or Torres Strait Islander	character
d_relation_s	demographic	relationship status	character
d_sex_birth_s	demographic	sex at birth	character
d_sexual_ori_s	demographic	sexual orientation	factor

Docs: Ingest, label and share model data

Thu, 05 Jan 2017 00:00:00 +0000

Docs: Share data via online repositories

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a vignette article from the ready4use library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

Note: This vignette is illustrated with fake data. The dataset explored in this example should not be used to inform decision-making.

library(ready4)
library(ready4use)

ready4use includes a number of tools for sharing health economic model data that forms part of the ready4 framework.

Identify data to be shared

To illustrate how to share data using ready4use classes and methods, we will first need some data to publish. In this example, we are going to share X, a Ready4useDyad (a data structure explained in another vignette) that we can create using data ingested from an online repository.

objects_ls <- ingest(Ready4useRepos(dv_nm_1L_chr = "fakes",
                                    dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/HJXYKQ",
                                    dv_server_1L_chr = "dataverse.harvard.edu",
                                    gh_repo_1L_chr = "ready4-dev/ready4",
                                    gh_tag_1L_chr = "Documentation_0.0"),
                     fls_to_ingest_chr = c("ymh_clinical_tb","ymh_clinical_dict_r3"),
                     metadata_1L_lgl = F)
X <- Ready4useDyad(ds_tb = objects_ls$ymh_clinical_tb,
                   dictionary_r3 = objects_ls$ymh_clinical_dict_r3) %>%
  renew()

We now specify where we plan to publish X in Y, a Ready4useRepos object (described in another vignette). Note, you must have write permissions to the repositories you specify in this step. The values entered in this example (the https://doi.org/10.7910/DVN/W95KED dataset from the fakes dataverse will not work for you).

Y <- Ready4useRepos(dv_nm_1L_chr = "fakes", # Replace with values for a dataverse & dataset for which
                    dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/W95KED", #  you have write permissions.
                    dv_server_1L_chr = "dataverse.harvard.edu")

We can now upload X to our preferred data repository using the share method. By default, if more than one data repository was specified in Y, then the dataverse repository will be preferred when sharing. We can overwrite this default by passing either “prefer_gh” or “all” values to the type_1L_chr argument. The Ready4useDyad object is now available for download at https://doi.org/10.7910/DVN/W95KED.

Y <- share(Y,
           obj_to_share_xx = X,
           fl_nm_1L_chr = "ymh_clinical_dyad_r4",
           description_1L_chr = "An example of a Ready4useDyad - a dataset (clinical youth mental health, AQoL-6D) and data dictionary pair. Note this example uses fake data.")