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LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild
<p><strong>LifeSnaps Dataset Documentation</strong></p> <blockquote> <p>Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the <strong>LifeSnaps dataset</strong>, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the <a href="https://rais-itn.eu/">European H2020 RAIS project</a>. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.</p> </blockquote> <p> </p> <p>The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.</p> <p><strong>Data Import: Reading CSV</strong></p> <p>For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">pandas.read_csv()</a> command.</p> <p><strong>Data Import: Setting up a MongoDB (Recommended)</strong></p> <p>To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.</p> <p>To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from <a href="https://www.mongodb.com/try/download/database-tools">here</a>.</p> <p>For the Fitbit data, run the following:</p> <pre><code>mongorestore --host localhost:27017 -d rais_anonymized -c fitbit <path to file fitbit.bson></code></pre> <p>For the SEMA data, run the following:</p> <pre><code>mongorestore --host localhost:27017 -d rais_anonymized -c sema <path to file sema.bson></code></pre> <p>For surveys data, run the following:</p> <pre><code>mongorestore --host localhost:27017 -d rais_anonymized -c surveys <path to file surveys.bson></code></pre> <p>If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.</p> <p><strong>Data Availability</strong></p> <p>The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:</p> <pre><code>{ _id: <ObjectId> id (or user_id): <ObjectId> type: <String> data: <Object> }</code></pre> <p>Each document consists of four fields: id (also found as user_id in sema and survey collections), type, and data. The _id field is the MongoDB-defined primary key and can be ignored. The id field refers to a user-specific ID used to uniquely identify each user across all collections. The type field refers to the specific data type within the collection, e.g., steps, heart rate, calories, etc. The data field contains the actual information about the document e.g., steps count for a specific timestamp for the steps type, in the form of an embedded object. The contents of the data object are type-dependent, meaning that the fields within the data object are different between different types of data. As mentioned previously, all times are stored in local time, and user IDs are common across different collections. For more information on the available data types, see the related publication.</p> <p><strong>Surveys Encoding</strong></p> <p><strong>BREQ2</strong></p> <p><em>Why do you engage in exercise?</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>engage[SQ001]</td> <td>I exercise because other people say I should</td> </tr> <tr> <td>engage[SQ002]</td> <td>I feel guilty when I don’t exercise</td> </tr> <tr> <td>engage[SQ003]</td> <td>I value the benefits of exercise</td> </tr> <tr> <td>engage[SQ004]</td> <td>I exercise because it’s fun</td> </tr> <tr> <td>engage[SQ005]</td> <td>I don’t see why I should have to exercise</td> </tr> <tr> <td>engage[SQ006]</td> <td>I take part in exercise because my friends/family/partner say I should</td> </tr> <tr> <td>engage[SQ007]</td> <td>I feel ashamed when I miss an exercise session</td> </tr> <tr> <td>engage[SQ008]</td> <td>It’s important to me to exercise regularly</td> </tr> <tr> <td>engage[SQ009]</td> <td>I can’t see why I should bother exercising</td> </tr> <tr> <td>engage[SQ010]</td> <td>I enjoy my exercise sessions</td> </tr> <tr> <td>engage[SQ011]</td> <td>I exercise because others will not be pleased with me if I don’t</td> </tr> <tr> <td>engage[SQ012]</td> <td>I don’t see the point in exercising</td> </tr> <tr> <td>engage[SQ013]</td> <td>I feel like a failure when I haven’t exercised in a while</td> </tr> <tr> <td>engage[SQ014]</td> <td>I think it is important to make the effort to exercise regularly</td> </tr> <tr> <td>engage[SQ015]</td> <td>I find exercise a pleasurable activity</td> </tr> <tr> <td>engage[SQ016]</td> <td>I feel under pressure from my friends/family to exercise</td> </tr> <tr> <td>engage[SQ017]</td> <td>I get restless if I don’t exercise regularly</td> </tr> <tr> <td>engage[SQ018]</td> <td>I get pleasure and satisfaction from participating in exercise</td> </tr> <tr> <td>engage[SQ019]</td> <td>I think exercising is a waste of time</td> </tr> </tbody> </table> <p><strong>PANAS</strong></p> <p><em>Indicate the extent you have felt this way over the past week </em></p> <table> <tbody> <tr> <td>P1[SQ001]</td> <td>Interested</td> </tr> <tr> <td>P1[SQ002]</td> <td>Distressed</td> </tr> <tr> <td>P1[SQ003]</td> <td>Excited</td> </tr> <tr> <td>P1[SQ004]</td> <td>Upset</td> </tr> <tr> <td>P1[SQ005]</td> <td>Strong</td> </tr> <tr> <td>P1[SQ006]</td> <td>Guilty</td> </tr> <tr> <td>P1[SQ007]</td> <td>Scared</td> </tr> <tr> <td>P1[SQ008]</td> <td>Hostile</td> </tr> <tr> <td>P1[SQ009]</td> <td>Enthusiastic</td> </tr> <tr> <td>P1[SQ010]</td> <td>Proud</td> </tr> <tr> <td>P1[SQ011]</td> <td>Irritable</td> </tr> <tr> <td>P1[SQ012]</td> <td>Alert</td> </tr> <tr> <td>P1[SQ013]</td> <td>Ashamed</td> </tr> <tr> <td>P1[SQ014]</td> <td>Inspired</td> </tr> <tr> <td>P1[SQ015]</td> <td>Nervous</td> </tr> <tr> <td>P1[SQ016]</td> <td>Determined</td> </tr> <tr> <td>P1[SQ017]</td> <td>Attentive</td> </tr> <tr> <td>P1[SQ018]</td> <td>Jittery</td> </tr> <tr> <td>P1[SQ019]</td> <td>Active</td> </tr> <tr> <td>P1[SQ020]</td> <td>Afraid</td> </tr> </tbody> </table> <p><strong>Personality</strong></p> <p><em>How Accurately Can You Describe Yourself?</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>ipip[SQ001]</td> <td>Am the life of the party.</td> </tr> <tr> <td>ipip[SQ002]</td> <td>Feel little concern for others.</td> </tr> <tr> <td>ipip[SQ003]</td> <td>Am always prepared.</td> </tr> <tr> <td>ipip[SQ004]</td> <td>Get stressed out easily.</td> </tr> <tr> <td>ipip[SQ005]</td> <td>Have a rich vocabulary.</td> </tr> <tr> <td>ipip[SQ006]</td> <td>Don't talk a lot.</td> </tr> <tr> <td>ipip[SQ007]</td> <td>Am interested in people.</td> </tr> <tr> <td>ipip[SQ008]</td> <td>Leave my belongings around.</td> </tr> <tr> <td>ipip[SQ009]</td> <td>Am relaxed most of the time.</td> </tr> <tr> <td>ipip[SQ010]</td> <td>Have difficulty understanding abstract ideas.</td> </tr> <tr> <td>ipip[SQ011]</td> <td>Feel comfortable around people.</td> </tr> <tr> <td>ipip[SQ012]</td> <td>Insult people.</td> </tr> <tr> <td>ipip[SQ013]</td> <td>Pay attention to details.</td> </tr> <tr> <td>ipip[SQ014]</td> <td>Worry about things.</td> </tr> <tr> <td>ipip[SQ015]</td> <td>Have a vivid imagination.</td> </tr> <tr> <td>ipip[SQ016]</td> <td>Keep in the background.</td> </tr> <tr> <td>ipip[SQ017]</td> <td>Sympathize with others' feelings.</td> </tr> <tr> <td>ipip[SQ018]</td> <td>Make a mess of things.</td> </tr> <tr> <td>ipip[SQ019]</td> <td>Seldom feel blue.</td> </tr> <tr> <td>ipip[SQ020]</td> <td>Am not interested in abstract ideas.</td> </tr> <tr> <td>ipip[SQ021]</td> <td>Start conversations.</td> </tr> <tr> <td>ipip[SQ022]</td> <td>Am not interested in other people's problems.</td> </tr> <tr> <td>ipip[SQ023]</td> <td>Get chores done right away.</td> </tr> <tr> <td>ipip[SQ024]</td> <td>Am easily disturbed.</td> </tr> <tr> <td>ipip[SQ025]</td> <td>Have excellent ideas.</td> </tr> <tr> <td>ipip[SQ026]</td> <td>Have little to say.</td> </tr> <tr> <td>ipip[SQ027]</td> <td>Have a soft heart.</td> </tr> <tr> <td>ipip[SQ028]</td> <td>Often forget to put things back in their proper place.</td> </tr> <tr> <td>ipip[SQ029]</td> <td>Get upset easily.</td> </tr> <tr> <td>ipip[SQ030]</td> <td>Do not have a good imagination.</td> </tr> <tr> <td>ipip[SQ031]</td> <td>Talk to a lot of different people at parties.</td> </tr> <tr> <td>ipip[SQ032]</td> <td>Am not really interested in others.</td> </tr> <tr> <td>ipip[SQ033]</td> <td>Like order.</td> </tr> <tr> <td>ipip[SQ034]</td> <td>Change my mood a lot.</td> </tr> <tr> <td>ipip[SQ035]</td> <td>Am quick to understand things.</td> </tr> <tr> <td>ipip[SQ036]</td> <td>Don't like to draw attention to myself.</td> </tr> <tr> <td>ipip[SQ037]</td> <td>Take time out for others.</td> </tr> <tr> <td>ipip[SQ038]</td> <td>Shirk my duties.</td> </tr> <tr> <td>ipip[SQ039]</td> <td>Have frequent mood swings.</td> </tr> <tr> <td>ipip[SQ040]</td> <td>Use difficult words.</td> </tr> <tr> <td>ipip[SQ041]</td> <td>Don't mind being the centre of attention.</td> </tr> <tr> <td>ipip[SQ042]</td> <td>Feel others' emotions.</td> </tr> <tr> <td>ipip[SQ043]</td> <td>Follow a schedule.</td> </tr> <tr> <td>ipip[SQ044]</td> <td>Get irritated easily.</td> </tr> <tr> <td>ipip[SQ045]</td> <td>Spend time reflecting on things.</td> </tr> <tr> <td>ipip[SQ046]</td> <td>Am quiet around strangers.</td> </tr> <tr> <td>ipip[SQ047]</td> <td>Make people feel at ease.</td> </tr> <tr> <td>ipip[SQ048]</td> <td>Am exacting in my work.</td> </tr> <tr> <td>ipip[SQ049]</td> <td>Often feel blue.</td> </tr> <tr> <td>ipip[SQ050]</td> <td>Am full of ideas.</td> </tr> </tbody> </table> <p><strong>STAI</strong></p> <p><em>Indicate how you feel right now</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>STAI[SQ001]</td> <td>I feel calm</td> </tr> <tr> <td>STAI[SQ002]</td> <td>I feel secure</td> </tr> <tr> <td>STAI[SQ003]</td> <td>I am tense</td> </tr> <tr> <td>STAI[SQ004]</td> <td>I feel strained</td> </tr> <tr> <td>STAI[SQ005]</td> <td>I feel at ease</td> </tr> <tr> <td>STAI[SQ006]</td> <td>I feel upset</td> </tr> <tr> <td>STAI[SQ007]</td> <td>I am presently worrying over possible misfortunes</td> </tr> <tr> <td>STAI[SQ008]</td> <td>I feel satisfied</td> </tr> <tr> <td>STAI[SQ009]</td> <td>I feel frightened</td> </tr> <tr> <td>STAI[SQ010]</td> <td>I feel comfortable</td> </tr> <tr> <td>STAI[SQ011]</td> <td>I feel self-confident</td> </tr> <tr> <td>STAI[SQ012]</td> <td>I feel nervous</td> </tr> <tr> <td>STAI[SQ013]</td> <td>I am jittery</td> </tr> <tr> <td>STAI[SQ014]</td> <td>I feel indecisive</td> </tr> <tr> <td>STAI[SQ015]</td> <td>I am relaxed</td> </tr> <tr> <td>STAI[SQ016]</td> <td>I feel content</td> </tr> <tr> <td>STAI[SQ017]</td> <td>I am worried</td> </tr> <tr> <td>STAI[SQ018]</td> <td>I feel confused</td> </tr> <tr> <td>STAI[SQ019]</td> <td>I feel steady</td> </tr> <tr> <td>STAI[SQ020]</td> <td>I feel pleasant</td> </tr> </tbody> </table> <p><strong>TTM</strong></p> <p><em>Do you engage in regular physical activity according to the definition above? How frequently did each event or experience occur in the past month?</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>processes[SQ002]</td> <td>I read articles to learn more about physical activity.</td> </tr> <tr> <td>processes[SQ003]</td> <td>I get upset when I see people who would benefit from physical activity but choose not to do physical activity.</td> </tr> <tr> <td>processes[SQ004]</td> <td>I realize that if I don't do physical activity regularly, I may get ill and be a burden to others.</td> </tr> <tr> <td>processes[SQ005]</td> <td>I feel more confident when I do physical activity regularly.</td> </tr> <tr> <td>processes[SQ006]</td> <td>I have noticed that many people know that physical activity is good for them.</td> </tr> <tr> <td>processes[SQ007]</td> <td>When I feel tired, I make myself do physical activity anyway because I know I will feel better afterwards.</td> </tr> <tr> <td>processes[SQ008]</td> <td>I have a friend who encourages me to do physical activity when I don't feel up to it.</td> </tr> <tr> <td>processes[SQ009]</td> <td>One of the rewards of regular physical activity is that it improves my mood.</td> </tr> <tr> <td>processes[SQ010]</td> <td>I tell myself that I can keep doing physically activity if I try hard enough.</td> </tr> <tr> <td>processes[SQ011]</td> <td>I keep a set of physical activity clothes with me so I can do physical activity whenever I get the time.</td> </tr> <tr> <td>processes[SQ012]</td> <td>I look for information related to physical activity.</td> </tr> <tr> <td>processes[SQ013]</td> <td>I am afraid of the results to my health if I do not do physical activity.</td> </tr> <tr> <td>processes[SQ014]</td> <td>I think that by doing regular physical activity I will not be a burden to the healthcare system.</td> </tr> <tr> <td>processes[SQ015]</td> <td>I believe that regular physical activity will make me a healthier, happier person.</td> </tr> <tr> <td>processes[SQ016]</td> <td>I am aware of more and more people who are making physical activity a part of their lives.</td> </tr> <tr> <td>processes[SQ017]</td> <td>Instead of taking a nap after work, I do physical activity.</td> </tr> <tr> <td>processes[SQ018]</td> <td>I have someone who encourages me to do physical activity. </td> </tr> <tr> <td>processes[SQ019]</td> <td>I try to think of physical activity as a time to clear my mind as well as a workout for my body.</td> </tr> <tr> <td>processes[SQ020]</td> <td>I make commitments to do physical activity.</td> </tr> <tr> <td>processes[SQ021]</td> <td>I use my calendar to schedule my physical activity time.</td> </tr> <tr> <td>processes[SQ022]</td> <td>I find out about new methods of being physically active.</td> </tr> <tr> <td>processes[SQ023]</td> <td>I get upset when I realize that people I love would have better health if they were physically active.</td> </tr> <tr> <td>processes[SQ024]</td> <td>I think that regular physical activity plays a role in reducing health care costs.</td> </tr> <tr> <td>processes[SQ025]</td> <td>I feel better about myself when I do physical activity.</td> </tr> <tr> <td>processes[SQ026]</td> <td>I notice that famous people often say that they do physical activity regularly.</td> </tr> <tr> <td>processes[SQ027]</td> <td>Instead of relaxing by watching TV or eating, I take a walk or am physically active.</td> </tr> <tr> <td>processes[SQ028]</td> <td>My friends encourage me to do physical activity.</td> </tr> <tr> <td>processes[SQ029]</td> <td>If I engage in regular physical activity, I find that I get the benefit of having more energy.</td> </tr> <tr> <td>processes[SQ030]</td> <td>I believe that I can do physical activity regularly. </td> </tr> <tr> <td>processes[SQ031]</td> <td>I make sure I always have a clean set of physical activity clothes.</td> </tr> </tbody> </table> <p><strong>Files Description</strong></p> <p><em>Scored Surveys:</em> CSV files containing scored versions of BREQ-2, IPIP, PANAS, STAI, and TTM surveys in scored_surveys folder</p> <p><em>Fitbit & EMA Data (daily granularity):</em> csv_rais_anonymized/daily_fitbit_sema_df_unprocessed.csv</p> <p><em>Fitbit & EMA Data (hourly granularity):</em> csv_rais_anonymized/hourly_fitbit_sema_df_unprocessed.csv</p> <p><em>MongoDB Dumps (compressed for practicality):</em> mongo_rais_anonymized/fitbit.bson for Fitbit data, mongo_rais_anonymized/sema for EMA data, and mongo_rais_anonymized/surveys for raw surveys data. </p> <p><strong>Code Availability</strong></p> <p><em>Data Anonymization:</em> <a href="https://github.com/syfantid/RAIS-Anonymization">https://github.com/syfantid/RAIS-Anonymization</a></p> <p><em>Exploratory Data Analysis:</em> <a href="https://github.com/kcristinaa/LifeSnaps-EDA">https://github.com/kcristinaa/LifeSnaps-EDA</a></p> <p><strong>Related Publications</strong></p> <p>Sofia Yfantidou, Christina Karagianni, Stefanos Efstathiou, Athena Vakali, Joao Palotti, Dimitrios Panteleimon Giakatos, Thomas Marchioro, Andrei Kazlouski, Elena Ferrari, and Sarunas Girdzijauskas, 2022, LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild (Submitted for peer-review).</p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813162. The content of this paper reflects only the authors' view and the Agency and the Commission are not responsible for any use that may be made of the information it contains. First and foremost, the authors would like to thank the participants of the LifeSnaps study who agreed to share their data for scientific advancement. The authors would like to further thank the web developers, T. Valk and S. Karamanidis, for their contribution to the project, all past and present RAIS fellows for their help with participants' recruitment, G. Pallis and M. Christodoulaki for their support with the ethics committee application, and B. Carminati for her feedback on data anonymization and privacy considerations.</p>
High resolution microsection images for: Common juniper, the oldest living non-clonal woody species across the tundra biome and the European continent
<p>Two high resolution images of the stem section are available as .czi files. These images are from a living <em>Juniperus communis</em> L. branch from Abisko (Sweden) sampled in August 2021. These high-resolution photographs (2.89 pixel/μm) were created using Axio Scan 7, Zeiss, Germany. </p> <p>One high resolution image of the same stem section is archived as a .tif file (49835x25587 pixels). This image is a composition of the two .czi images created using Axio Scan 7, Zeiss, with a reduced resolution and edited adding the ring-count reference points and the reference scale.</p>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption</p>
BetaEddyOne: A long-lived 1.5 layer quasigeostrophic eddy on a beta plane, with Lagrangian particles
<p>BetaEddyOne is a yearlong simulation of a large, nonlinear oceanic eddy under 1.5 layer quasigeostrophic dynamics on a beta plane, initially located at 24˚N. The simulation is run at 512 x 256 resolution and is seeded with Lagrangian particles with one particle per grid point. </p> <p>This simulation is indended for use as a common test case for eddy-related diagnostics and analysis methods. It approximately replicates the eddy analyzed in detail in Early, Samelson, and Chelton (2011), <a href="https://doi.org/10.1175/2011JPO4601.1">https://doi.org/10.1175/2011JPO4601.1</a>. The simulation was created using the WaveVortexModel (Early, Lelong, and Sundermeyer, 2021, <a href="https://doi.org/10.1017/jfm.2020.995">https://doi.org/10.1017/jfm.2020.995</a>), the code for which is available on GitHub at <a href="https://github.com/Energy-Pathways-Group/GLOceanKit">https://github.com/Energy-Pathways-Group/GLOceanKit</a>. This particular simulation was created for use in the paper</p> <p>Lilly, J. M., J. Feske, B. Fox-Kemper, and J. J. Early (2024). Integral theorems for the gradient of a vector field, with a fluid dynamical application. <em>Proceedings of the Royal Society of London, Series A</em>. <strong>480</strong> (2293): 20230550, 1–30. <a href="https://doi.org/10.1098/rspa.2023.0550">doi 10.1098/rspa.2023.0550</a>.</p> <p>The figure shows a snapshot of the model's vertical vorticity. </p> <p> </p>
Self-reported data for Sustainable Development from people living in rural and remote areas
<p>Anonymous self-reported data from people living in rural/remote areas as part of a research project on Sustainable Development.</p> <p>The data collection has been approved by the University of Technology Sydney (UTS HREC REF NO. ETH24-9191).</p> <p>The version 1.0 of the dataset includes 212 valid answers to 40 core questions (+ additional info) collected in 2024 in Saudi Arabia.</p>
Vagrant Lives: 14,789 Vagrants Processed by Middlesex County, 1777-1786 (version 1.2)
<p><em>This dataset should be used instead of the earlier version (https://zenodo.org/record/13103).</em></p> <p>This updated dataset makes accessible the uniquely comprehensive records of vagrant removal from, through, and back to Middlesex, encompassing the details of some 14,789 men and women removed (either forcibly or voluntarily) as undesirables between 1777 and 1786. In includes people ejected from London as vagrants, and those sent back to London from counties beyond. Significant background material is available on the 'London Lives' website, which provides additional context for these records. The authors also recommend the following article:</p> <p> Tim Hitchcock, Adam Crymble, and Louise Falcini, ‘Loose, Idle and Disorderly: Vagrant Removal in Late Eighteenth-Century Middlesex’, _Social History_.</p> <p>Each record includes details on the name of the vagrant, his or her parish of legal settlement, where they were picked up by the vagrant contractor, where they were dropped off, as well as the name of the magistrate who had proclaimed them a vagrant. Each entry is georeferenced, to make it possible to follow the journeys of thousands of failed migrants and temporary Londoners back to their place of origin in the late eighteenth century.</p> <p>Each entry has 31 columns of data, all of which are described in the READ ME file.</p> <p>The original records were created by Henry Adams, the vagrant contractor of Middlesex who had - as had his father before him - conveyed vagrants from Middlesex gaols to the edge of the county where they would be sent onwards towards their parish of legal settlement. His role also involved picking up vagrants on their way back to Middlesex, expelled from elsewhere, as well as those being shepherded through to counties beyond, as part of the national network of removal. Eight times per year at each session of the Middlesex Bench, Adams submitted lists of vagrants conveyed as proof of his having transported these individuals, after which he would be paid for his services. The dataset contains all 42 surviving lists out of a possible 65.The gaps in the records are unfortunately not evenly spaced throughout the year. We know more, for example, about removal in October than in May.</p> <p>Spellings have been interpreted and standardized when possible. Georeferences have been added when they could be identified. This dataset was created for 21st century historians, and should not be construed as a true transcription of the original sources. Instead the goal was to use a limited vocabulary and to interpret the entries rather than recreate them verbatim. While this is undesirable for anyone interested in spelling variations of names and place names in the eighteenth century, it is the authors' hope that these interpretations will make it easier to conduct quantitative analysis and studies in historical geography.</p> <p>This dataset has been published with additional contextual information in the <em>Journal of Open Humanities Data</em>. It can be found at the following location:</p> <p>Crymble, A, Falcini, L and Hitchcock, T 2015 Vagrant Lives: 14,789 Vagrants Processed by the County of Middlesex, 1777–1786. <em>Journal of Open Humanities Data</em> 1: e1, DOI: http://dx.doi.org/10.5334/johd.1</p>
Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles
<p>Data corresponding to the figures of the publication "All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles" by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
London Lives Pauper Examinations
<p>This release includes plain text files of examinations and a supplementary dataset of St Clement Danes removal orders.</p>
Evaluating Multi-Tenant Live Migrations Effects on Performance
<p>Results for Evaluating Multi-Tenant Live Migrations Effects on Performance article (Coopis 2018)</p> <p>Framework used to generate this data available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms</a></p> <p>Code for data interpretation available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms/coopis2018/xp_paper.ipynb</a></p> <p>Files description :</p> <ul> <li>png images : BPM process schemas used for the experimentations (AdditionalApproval, HumanTask and M3Process)</li> <li>xp1.csv : data for the <em>Migration duration </em>experiment</li> <li>xp3.csv : data for the <em>Migration effects on migrated tenant</em> and <em>Migration effects on co-located tenants</em> experiments</li> </ul>
ENERGISE Living Lab country report - Denmark
<p>The Danish ELLs were conducted in Roskilde, where ELL1 was situated in Viby Sjælland, and ELL2 was situated in Trekroner. In ELL1 18 participants were involved, and in ELL2 20 participants were involved. In ELL1, participants mainly lived in detached, privately owned houses, where as participants in ELL2 primarily lived in privately owned terraced hoses. The buildings in ELL1 are older than the buildings in ELL2, and the houses in ELL2 are slightly smaller than the houses in ELL1. There is a mix of household sizes and compositions in each ELL, where the average age of participants in ELL1 is slightly older than the average age of ELL2 participants. ELL1 can be considered a community of place, whereas ELL2 can be considered a community of interest, as ELL2 participants consider themselves to community-builders and to be slightly greener than the average population. This is, however, not necessarily so, as this report will also demonstrate.</p>
ENERGISE Living Lab country report _ Netherlands
<p>ENERGISE Living Labs (ELLs) employ practice-based approaches to reduce energy use in households while co-creating knowledge on why energy-intensive practices are performed and how they depend on the context in which they are performed. Altogether 16 living labs were implemented in eight European countries in 2018.</p> <p><br> The Dutch ELLs were led by the ENERGISE team from Maastricht University, in Maastricht in the Netherlands. The ENERGISE Living Labs were implemented in the Southern most province in the Netherlands, Limburg – in two municipalities. Maastricht for ELL1 and Roermond for ELL2, the community-based ELL. Participants were recruited with the help of a local implementation partner Op het Zuiden.</p>
Locked Shields Partners Run 23 (LSPR23): A novel IDS dataset from the largest live-fire cybersecurity exercise
<p>IDS Dataset from the Largest Live Fire Cybersecurity Exercise Using Virtual Blue Team Network Traffic.<br><br></p> <ul> <li> <p>LSPR23 is derived from Locked Shields 2023, a major live-fire cyber defense exercise.</p> </li> <li> <p>LSPR23 includes ~16M network flows, of which ~1.6M are labeled malicious.</p> </li> </ul> <p> </p> <p>Please cite our research article:"LSPR23: A novel IDS dataset from the largest live-fire cybersecurity exercise" when using our dataset:<br>https://doi.org/10.1016/j.jisa.2024.103847<br><br><br></p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2021
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
More social species live longer, have longer generation times, and longer reproductive windows
<p>Data and scripts required to reproduce the results of the manuscript "More social species live longer, have longer generation times, and longer reproductive windows"</p>
Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic
<p>The data in this repository is part of the paper titled "Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic". The data is required to obtain a detrended lockdown effects on air quality. The raw data was downloaded from the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 (EAC4) product portal. More details of the data are listed below:</p> <p>"ozone_data.nc": Global mixing ratio of ozone (monthly)</p> <p>"pm_data.nc": Global mass concentration of fine particulate matters, and aerosol optical depth (AOD) at 550 nm (monthly)</p> <p>"BAU_clean_latest.csv": The pollution level under a business-as-usual (BAU) scenario, inferred from the historical pollution data by Theil-Sen linear regression (monthly)</p>
Song capturing lived-experiences of flooding and climate resilience with St. Eugenes Choir Newtownstewart (BluePrint project)
<p>This audio piece represents one of the creative risk communication outputs co-created within the BluePrint project. Between March and October 2024, socially engaged artist Sara Walmsley worked creatively with flood-affected community representatives in Newtownstewart, Co. Tyrone and Eglinton, Co. Derry-Londonderry exploring their lived-experiences of flooding and need for climate adaptation and resilience. </p> <p>In the audio piece, you will hear the melodic, polyphonic harmonies of St. Eugene’s Church choir (Newtownstewart) as they give music to the words of members of their community whose homes were destroyed and lives endangered by flood water. The piece captures the voices of those striving to adapt to our changing climate, those who are responding to the urgency by finding solace, hope, strength and courage in the unending and unsurprising resilience and creativity of our communities. </p> <p>The BluePrint project is led by the MaREI Centre, University College Cork, with partners the Playhouse, Derry City and Strabane District Council, and Mayo County Council. The BluePrint project is a recipient of the Creative Climate Action fund, an initiative from the Creative Ireland Programme. It is funded by the Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media in collaboration with the Department of the Environment, Climate and Communications. </p> <p>Find out more: <a href="https://www.marei.ie/project/blueprint/">https://www.marei.ie/project/blueprint/</a></p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a vertical concentrated live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Cuckoo male bumblebees perform slower and longer flower visits than free-living male and worker bumblebees
<p>These .txt files include the dataset (tab-separated) and the annotated R-scripts (R-scripts_R1 is the final version) used in the analyses reported in the preprint "Cuckoo male bumblebees perform slower and longer flower visits than free-living male and worker bumblebees".</p> <p>The preprint is available on Zenodo (<a href="https://doi.org/10.5281/zenodo.4489066">https://doi.org/10.5281/zenodo.4489066</a>) and has been recommended by PCI Zoology (<a href="http://zool.peercommunityin.org/articles/rec?id=44">https://zool.peercommunityin.org/articles/rec?id=44</a>). The article has then been published in the Belgian Journal of Zoology (2021, 151:193:203, <a href="https://belgianjournalofzoology.eu/index.php/BJZ/article/view/93">https://doi.org/10.26496/bjz.2021.93</a>)</p> <p> </p>
Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'
<p><strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong></p> <p><em>Running the Code</em></p> <p>SSL_Demo_2.m : main program for self-supervised machine learning segmentation</p> <p>SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m)</p> <p>This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background.</p> <p>It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure.</p> <p>Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run.</p> <p><em>Code Methodology</em></p> <p>The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required.</p> <p>Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination.</p> <p>The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy.</p> <p>Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions.</p> <p>The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing): </p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a></p> <p>This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.