Experience sampling data allows researchers to map psychological dynamics across diverse everyday contexts, giving us insights into life outside the lab. But these data are costly to collect in terms of both time and money. As such, it is ideal to be able to get the most out of an experience sampling dataset by returning to it with new research questions. This potential is multiplied when we have access to a large number of datasets in which to replicate our analyses. Sharing your data ensures that researchers get the maximum use out of these data, increasing its contribution to scientific knowledge and benefitting the scientific community at large. By adding your data to EMOTE, you will become part of a worldwide network of researchers contributing to open and rigorous science.

Data owners have the option to include a citation to a key publication using the data, which potential data users will be instructed to cite when using the data for publications, presentations, or other media. Data owners will also be provided with metrics on how many times their data has been requested to demonstrate impact. These metrics are available on the page for your dataset. In addition, if they choose to do so, data owners will be able to vet and approve requests to use their data.

If your data meets the following requirements, we’d love to include it in EMOTE! (and see our Data Deposit page for more details)

• Was your data collected using intensive longitudinal methods: experience sampling (aka. ecological momentary assessment) or daily diary methodologies? (see FAQ “What type of data can I upload to EMOTE?” for more information)
• Does your ethical clearance allow you to share your de-identified data?
• Have you removed all identifying information about participants? (see FAQ “How do I remove identifying information from my dataset?)

Please make sure your data is de-identified by following these steps:

• You do not include any specific identifying information. This includes things like name, email address, home or work address, or identification numbers from online data collection services (like MTurk and Prolific). This is not an exhaustive list, and we ask that you carefully check your data for anything that could be used to identify the participant before submission.
• Demographic information (e.g. nationality, gender identity) may be included as long as the information cannot be linked back to the participant. Date of birth information should be replaced with age in years. Review any demographic information included for any information that is enduring (that is, that doesn’t change about a person) and/or asks for a very specific answer (for example, a specific job title).
• Review any open-text or string variables to ensure that participants have not accidentally included identifying information in these fields (e.g. entering email address by accident in a field meant for something else).
• Review whether a researcher could slice across all the demographic variables available to identify a participant. One particular demographic variable might not be an issue, but in combination with the other demographics in the dataset, and the year and context the data were collected in, there might be more cause for concern (particularly for participants from under-represented groups).

For further information on identifying information please refer to the EU General Data Protection Regulation on personal data and this useful University of Sydney guide on removing identifiers from data.

As an additional cautionary step, the unique identifiers that you use to identify each participant will be changed by the EMOTE Database team before upload.

When uploading data to the EMOTE database, we request researchers adhere to the following instructions to ensure data is uploaded quickly and is easily searchable by other users.

A single CSV data file encoded in UTF-8 format should be submitted per project with data arranged in long format wherein each row represents a single experience sampling methodology (ESM) timepoint. If baseline or follow-up data was collected in addition to ecological data, these data should be added to each row of ESM data.

Please follow the following conventions in the data:

• Where available, please upload only the raw version of the data: that is, before any transformations, scale creation, reverse-scoring etc. This ensures that any potential mistakes made in the transformation process aren’t perpetuated across data-users, and allows anybody using the data to create and check scales from first-principles
• Missing values are left empty or replaced by NA or NaNs.
• Include a line for surveys that were sent to participants but not completed, with all relevant variables set to missing data (allowing users to create correctly lagged variables)
• An ID variable is included per participant in a column labelled “uID”
• If possible, the following date and time variables (if you don’t have this information for your data, please include these columns but leave them blank. If you have issues converting your data to these formats, we can help - get in touch!)
• A variable called Date_Local that includes the date that the ESM survey was collected, preferably formatted as YYYY-MM-DD, and in the participants’ local time-zone
• A variable called Time_Local that includes the time that the ESM Survey was collected, preferably formatted as HH:MM:SS (or HH:MM), using 24-hour time, and in the participants’ local time-zone

Please ensure that the ethics approval at your institution permits the sharing of data. To upload the data you will need to verify that you have ethical clearance to do so. Please note that even if you did not originally obtain permission to share your data when they were collected, it is often possible to apply for such permission retrospectively.

Currently, the EMOTE database accepts any form of data collected using intensive longitudinal methods with human participants (e.g. experience sampling methods, ecological momentary assessment, diary methods, ambulatory assessment). These methods are characterized by repeated measurements collected about the same participants during their everyday lives. These methods focus on capturing people’s experiences, including thoughts, feelings, environments, and interactions in day-to-day life through repeated measurement. We are interested in any dataset with at least one measurement occasion per day.

We currently have a 1GB data upload limit, and so some very large additional data sources (e.g. fMRI or psychophysiology data) may be difficult to share on EMOTE. These additional data collected as part of an ESM/EMA study can be linked in the Data Deposit submission. You may include URL’s if your data is stored elsewhere (e.g. heart rate variability data stored and accessible on github) or invite users to contact you for additional data you wish to share.

If you are unsure whether your data is suitable for EMOTE, please contact us at emote-database@unimelb.edu.au

More details on our preferred data upload format can be found here

Data can be uploaded here. This form will walk you through the steps of the process, including how you should format your data.

Data deposit involves the following steps, which you will be walked through when you begin the process at the data deposit page:

1. Read information about the requirements for EMOTE data
2. Confirm that your data meets the requirements and accept the Terms & Conditions for data upload
3. Fill out a data deposit form with your details, and some details about your dataset
4. Upload the file
5. We will generate a variable information template from your datafile, and email you the template with some instructions for how to fill it out. We’ve made this template as minimal as possible, with a few compulsory fields, as well as some voluntary fields if you’d like to give us a bit more information
6. You’ll upload that variable information template, and we’ll run it through our construct-coding algorithm, which will suggest construct tags for your data to enable easy searching, as well as put your data through some automated quality control checks. If any issues arise in this process, we’ll get in touch with you
7. You’ll have three weeks to review the automated construct tags we’ve suggested (you can approve, reject, or change the tags) - if you choose not to review the tags, we’ll make the data available with the automated tags only
8. Your data will become available in EMOTE

We’re always looking for ways to streamline this process! If you’d like to donate some grant funding towards supporting us in developing this process further, please get in touch with us at emote-database@unimelb.edu.au

Currently we are able to accept CSV files up to 1GB in size. If your data are over 1 GB, please get in touch with us to discuss at emote-database@unimelb.edu.au

If you would like to add or remove all or some of the data, or if you have noticed any errors, please get in touch with us at emote-database@unimelb.edu.au. There will be a change log associated with each dataset. Data users who have already downloaded the data should check back to see if any changes or additions have been made.

If requested, we can remove data you have uploaded to EMOTE. However, we cannot remove data from users who have already downloaded the data. As the data owner, you are welcome to get in touch with any data users who have downloaded the data and request that they delete it.

Yes. As data owner you can see all data request forms for your data.

As a data owner, you have the option to opt in to be an admin on your dataset. This means that you will be able to approve who has access to your data. If you choose to be an admin, we ask that you respond to any data requests within three weeks. If you do not respond within three weeks, the EMOTE database team will decide whether to share the data without your input. If you choose not to opt in to be an admin on your dataset, this process will be handled solely by the EMOTE database team (see below for more information on the process).

Please include your contact information with the dataset, so users can directly reach you in case of any questions/queries.

There are several ways to browse EMOTE:

• Check out our list of datasets. You can filter by constructs, scale names, study names, number of assessments, and more.
• You can look at our list of constructs or our network visualisation of constructs to find what you are interested in, and see in which datasets your constructs are available
• You can search by variable names, constructs, study names, and more using the search box on our home page

The list of available datasets is increasing continually, so make sure to check back here soon. If you would like to contribute to EMOTE by sharing your data we would be most grateful.

Before requesting data, you’ll need to create an account. To do this, you’ll need an ORCID ID: if you don’t have one already, you can easily create one at this link.

When you find a variable you’re interested in, you’ll be able to add that variable to your cart, which you can see in the toolbar at the top of the page. You can add many things to your cart, and you can click on the cart to delete things that you’ve added, or look over what you currently have attached to your request.

When you have decided on which variables you’d like to request, you can click on the data request form, which is also available at the bottom of the cart page.

The data request form outlines the intended use of the data, as well as the exact datasets and variables you are interested in. You’ll also need to accept our data request Terms & Conditions, and your co-authors will need to accept a co-author Terms & Conditions. Once submitted, the request form will be reviewed by the EMOTE database team. For some datasets the data depositor will also review the application. We aim to provide application outcomes via email within three weeks, although this will often happen more quickly.

As outlined in our Terms & Conditions, this database is available for research and teaching purposes only. The data cannot be used for commercial purposes.

We have an application process for multiple reasons:

• We are committed to promoting open and rigorous scientific practices. Having an application process ensures that data users have a clear objective before data analysis begins.
• We are making EMOTE available for research and teaching purposes only: the application process allows us to determine whether requests fit these terms.
• An application process also allows data owners to retain control over the sharing of their data, which encourages more data owners to share their data.
• Finally, an application process allows us and/or data owners to make sure that multiple users are not attempting to answer the same research questions with the same data, reducing overlap.

Yes! However, currently to do this, you will have to submit a new data request form: it is not yet possible to update your already submitted form. In your form, please enter the request number of your previous request, and note that this is an update to that previous request.

We aim to process your data request form within 3 weeks (but this usually occurs more quickly).

Each dataset will contain a longform csv file containing an unique identifier variable (called uID) and a date and time stamp for each experience sampling assessment (called Date_Local and Time_Local). For each variable you request, you will receive the wording of individual items (or a description of the variable, if it is not a self-report item), the assessment time point, the assessment type, the response type, as well as the actual responses for the variables you have requested. All responses will be deidentified to ensure anonymity.

We endeavor only to include the raw data: we have removed all computed scales, reverse-coding, and transformations. This is to avoid perpetuating any mistakes across data-users. This means that you will need to do any data transformations yourself after receiving the data.

There will be a change log associated with each dataset. Data users who have already downloaded the data should check back regularly to see if any changes or additions have been made.

No! There may not be such a thing as a free lunch, but luckily there is such a thing as free data.

We are thankful for the incredible team who helped build the EMOTE database and the support that we have received thus far, primarily from the University of Melbourne, as well as the Research Group of Quantitative Psychology and Individual Differences at KU Leuven. However, if you find that EMOTE has contributed positively to your work and forms the basis of any future grant proposals, we would appreciate any post-payment from your grants. Such contributions would go towards the maintenance and growth of EMOTE. Please contact us at emote-database@unimelb.edu.au if you would like to know more.

Finally, we require that you cite the EMOTE database in any publications, presentations, or other media that result from this data.

If we are aware of an existing approved application which investigates the same research topic, we will not approve any other applications. However, we cannot ensure that no other users are investigating the same topic.

No, as long as these are appropriately justified in light of your planned research question.

Yes! But we ask that if you do so, you cite both the EMOTE database and the original citation associated with the dataset(s) in your publication. We also suggest that you include the following sentence in your publications: “These data are part of the EMOTE database. Data to replicate these findings is available upon request at the EMOTE database (quote data request number [include your data request number here])”

You can find information on how to cite the EMOTE database here [link to citation on about page here].

You are not able to share these data with anybody who is not listed on your data request form, or to link the data on any other repository (e.g. OSF, GitHub)

If you would like to share a link to your datafile for the purpose of peer review or replication, you will need to ask the reviewers/replicators to put in a data request to EMOTE. To speed up this process, we suggest that you quote your data request number in any publications using EMOTE data using a sentence like this: “These data are part of the EMOTE database. Data to replicate these findings is available upon request at the EMOTE database (quote data request number [include your data request number here])”. Then, potential replicators can get in touch with us using that data request number and we will release the identical data to them. They can do this by putting in a data request form quoting your data request number and indicating the data are for peer review or replication.

If you would like to share a dataset directly with your code on a repository so that analyses can be replicated, we suggest creating a synthetic version of the dataset that mimics its original properties.

The description of each dataset includes some details about the procedure. Furthermore, if the data has resulted in any publications, a citation will be included in the description of the dataset. This publication will often describe the procedure in more detail than we have provided. If these details are not sufficient, you can contact the data owner for more information: the email for the data owner will be listed on the about page for the dataset.

These suffixes indicate the assessment type:

• GL - general trait variable assessment
• BL - baseline data
• ES - ESM (experience sampling method) data
• ES2 - ESM data collected in the second wave
• ES3 - ESM data collected in the third wave
• SC - data collected from screening questionnaires
• FO - follow-up data
• FO2 - follow-up data collected in the second wave

The EMOTE database was created by the Functions of Emotion in Everyday Life (FEEL) Lab at the Melbourne School of Psychological Sciences, University of Melbourne in collaboration with the Melbourne Data Analytics Platform (MDAP), and the Research Group of Quantitative Psychology and Individual Differences at KU Leuven. The team is Dr Elise Kalokerinos, Dr Daniel Russo-Batterham, Dr Peter Koval, Dr Paul Garrett, Ms Komal Grewal, Dr Katharine Greenaway, Dr Aleks Michalewicz, Dr Jonathan Garber, and Professor Peter Kuppens.

This database would not be possible without the following contributors:

• 2020 Collaboration Grant from the Melbourne Data Analytics Platform at the University of Melbourne
• An Australian Research Council (ARC) Discovery Early Career Researcher Award (DE180100352) awarded to Elise Kalokerinos, an ARC Discovery Project (DP160102252) and Discovery Early Career Researcher Award (DE190100203) awarded to Peter Koval, an ARC Future Fellowship (FF190100300) awarded to Katharine Greenaway, and an ARC Discovery Project awarded to Katharine Greenaway and Elise Kalokerinos (DP190103072).

Please see the about page for the latest citation information, and please cite us when including data from the EMOTE database in publications, presentations, and other media.

For each dataset you use, please also cite the original citation provided by the data owner. This encourages more data owners to freely share their data, and for us to track other publications using each individual dataset.

We’d love to hear from you! Get in touch here: emote-database@unimelb.edu.au. Please note that EMOTE is run by a small team and with limited funding, so we’re limited in what we are able to implement, but we’re always hoping to expand! If you have requests and are able to fund them, we will be able to prioritize your request.

You can find more information on how your information is used and stored in the Privacy Statement that is displayed prior to submitting your information.

Only the EMOTE database team and the data depositor for each dataset will have access to this information.

Data depositors are asked to follow a set of instructions to make sure that all identifying information has been removed. For more information, see the FAQ on de-identifying data.