Find research datasets worth reusing

Modelled gas hydrate stability zone extents for Svalbard's fjord and near-shore settings

Supplementary data to the published paper 'Modelling of the gas hydrate potential in Svalbard's fjords' (https://doi.org/10.1016/j.jngse.2021.104127). This archive includes geopackage files for each of the targeted fjords and contains (amongst others) bathymetric depth (Z, metres), top and base of the modelled GHSZ (GHSZ_T/GHSZ_B, metres depth), cumulative GHSZ cell count (#, 25 metres per), and the number of CTD data points within the radius (CTD_count and CTD2deep). Included onshore modelling data is to be ignored and considered a modelling artefact.

Set of photos (123) of surface artifacts and cranium bones in the Iroungou burial cave, Mouila area, Gabon

Photographs (annotated) of all 182 artifacts (iron currency knives, hoes, iron and copper bracelets, rings, kindu, gong) and 51 cranium bones visible on the ground during the initial visit of the Iroungou burial cave, Mouila area, Gabon, on september 23, 2018.

opencc-by-4.0Dec 2020View details →

Evidence summary on activities or settings associated with a higher risk of SARS-CoV-2 transmission: Summary of included evidence syntheses

This is a data extraction table associated with the HIQA report entitled: Evidence summary on activities or settings associated with a higher risk of SARS-CoV-2 transmission

opencc-by-nc-4.0Jul 2021View details →

3-D synthetic near surface data set with frequency-domain electromagnetic induction data

Realistic three-dimensional exhaustive data set that mimics a near surface mining landfill deposit of waste fine-shaly sands. The data set is composed by petrophysical properties and frequency domain electromagnetic induction (FDEM) data and was created with the purpose of testing algorithms for near-surface modeling and characterization using electromagnetic data. The set of petrophysical properties include porosity, water saturation, particle density and density. Each property corresponds to a single geostatistical realization. The three-dimensional model has a dimension of 150 by 200 by 4 meters (i.e., length, width, depth) with a cell size of 0.5 m by 0.5 m by 0.1 m, respectively (grid size of 300 x 400 x 40). The model grid has 4.8 million cells. Porosity and particle density were modelled based on samples of fine-shaly sands collected at a mine tailing in Portugal for which we investigated porosity, specific weight and particle density. The results of these investigations were used to generate three-dimensional models of subsurface rock properties with unconditional stochastic sequential simulation (Deutsch & Journel, 1998). Porosity was modelled with an omnidirectional spherical variogram model in the horizontal direction. The variogram model has a horizontal range of 10 m, a vertical range of 1 m and a nugget effect of 0.2 % of the total variance of the data. This variogram model describes the expected spatial distribution of this property in the mine tailing. To ensure plausibility between rock properties, particle density and water saturation models were generated with stochastic sequential co-simulation (Deutsch & Journel, 1998) conditioned to the porosity model. For particle density we imposed an omnidirectional spherical variogram model in the horizontal direction with a range of 10 m, a vertical range of 1 m and a nugget effect of 0.2 % of the total variance of the data, and the correlation between porosity and particle density from the lab measurements. For water saturation we imposed an omnidirectional spherical variogram model in the horizontal direction with a range of 16 m, a vertical range of 2 m and a nugget effect of 0.1 (%). For the co-simulation we imposed a correlation between porosity and water content, borrowed from Bhanbhro et al. (2013) and Dumont et al. (2016). The pore fluid was defined as consisting in 80% of water and 20% of leachate, having a density of 0.99114 g/cm3 at a temperature of 30ºC (Souza et al., 2014). The density was mathematically calculated from porosity and particle density models and the density of the pore fluid by using a simple volumetric average of the geological material densities and its relationship to porosity (Mavko et al., 2009),  db = (1 - Ø) d0 Ø dfl  , where d0 is the density of the mineral grains, dfl is the density of the pore fluids, and Ø is porosity. The electrical conductivity (EC) was created based on the well-known empirical relationship of Archie’s law (Archie, 1942). We first calculate electrical conductivity using the following equation, Rt = a Sw-n Ø-m Rw  , where a is the tortuosity constant, assumed as 0.88, Sw is the water saturation, n is the saturation exponent, assumed as 2, Ø is the porosity, m is the cementation exponent, assumed as 1.37, and Rw is the electrical resistivity of the pore fluid, assumed as 0.25. From the lithology and range of porosity values of the mining landfill model, the values of a, n and m were defined from Keller (1987). The electrical resistivity of the pore fluid was defined based on its composition and density (Keller, 1987). The EC was calculated based on Archie´s second law (Archie, 1942), where conductivity of the partially saturated rock (ct) is the inverse of its resistivity (Rt),  ct = 1 / Rt   (Mavko et al., 2009). Since the relationship between magnetic minerals and the magnetic properties of the rocks depends primarily of the composition and grain size of them (Butler, 2005), the magnetic susceptibility (MS) was modelled using the common range of magnetic susceptibility for unconsolidated sediments (Hudson et al., 1999) with unconditional stochastic sequential simulation (Deutsch & Journel, 1998), imposing an omnidirectional spherical variogram model in the horizontal direction with a range of 20 m, a vertical range of 4 m and a nugget effect of 0.1 % of the total variance. From the resulting three-dimensional models of EC and MS, we retrieved nine equally spaced boreholes along the same yz profile. These borehole data might be used as experimental data for modelling workflows, including geophysical inversion. FDEM data, both the in-phase (IP) and quadrature-phase (QP), were calculated using a 1-D forward model (Hanssens et al., 2019). The acquisition configuration replicates one of the most common sensors for FDEM near-surface surveys, namely the DUALEM-421S (DUALEM Inc., Milton, Canada). It considers two loop-loop coil orientations, a horizontal coplanar (HCP) and a perpendicular one (PRP), with the normal 3 offsets per coil orientation for this equipment, 1, 2 and 4 meters for HCP, and 1.1, 2.1 and 4.1 meters for PRP, plus an extra offset per coil orientation, 10 meters for HCP and 10.1 meters for PRP, ensuring a theoretical larger depth of investigation. The FDEM data were calculated defining the operating frequency of the sensor as 9000 Hz, with an elevation to the surface of 0.15 m.

Accompanying data set for the manuscript "REverSe TRanscrIptase Chain Termination (RESTRICT) for Selective Measurement of Nucleotide Analogs Used in HIV Care and Prevention"

This data set contains all experimental and theoretical data included in the manuscript " REverSe TRanscrIptase Chain Termination (RESTRICT) for Selective Measurement of Nucleotide Analogs Used in HIV Care and Prevention", namely: RESTRICT_model: MATLAB script for completing calculations in the RESTRICT theoretical model. Figure 2: <ul> <li>Raw data from theoretical model showing contributions of individual model components, Kaff = 0.3</li> <li>Normalized data from theoretical model showing contributions of individual model components, Kaff = 0.3</li> <li>Experimental NRTI Drug Screen 180 nt TTCA 500 nM dNTP</li> </ul> Figure 3: <ul> <li>Experiment-dNTP-Concentration-Screen</li> <li>Theory-dNTP-Concentration-Screen</li> <li>Experiment-Template-Length-Screen</li> <li>Theory-Template-Length-Screen</li> <li>Experiment-Sequence-Screen</li> <li>Theory-Sequence-Screen</li> </ul> Figure 4: <ul> <li>Experimental-NRTI-Drug-Screen-90nt-TCAA-only</li> <li>Theory-TCAA90-Kaff=0point2</li> <li>Experimental-NRTI-Drug-Screen-GGCA-only</li> <li>Theory-GGCA180-Kaff=0point2</li> </ul> Figure 5: <ul> <li>GGCA-vs-TTCA-Specificity-Analysis</li> </ul>

Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning

This Zenodo dataset contain the Common Objects in Context (COCO) files linked to the following publication: Verhaegen, G, Cimoli, E, & Lindsay, D (2021). Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning. Biodiversity Data Journal. Each COCO zip folder contains an "annotations" folder including a json file and an "images" folder containing the annotated images. Details on each COCO zip folders: <ul> <li>Beroe_sp_A_images-coco 1.0.zip</li> </ul> COCO annotations of Beroe sp. A for the following 114 images: MCMEC2018_20181116_NIKON_Beroe_sp_A_c_1 to MCMEC2018_20181116_NIKON_Beroe_sp_A_c_16, MCMEC2018_20181125_NIKON_Beroe_sp_A_d_1 to MCMEC2018_20181125_NIKON_Beroe_sp_A_d_57, MCMEC2018_20181127_NIKON_Beroe_sp_A_e_1 to MCMEC2018_20181127_NIKON_Beroe_sp_A_e_2, MCMEC2019_20191116_SONY_Beroe_sp_A_a_1 to MCMEC2019_20191116_SONY_Beroe_sp_A_a_28, and MCMEC2019_20191127_SONY_Beroe_sp_A_f_1 to MCMEC2019_20191127_SONY_Beroe_sp_A_f_12 <ul> <li>Beroe_sp_B_images-coco 1.0.zip</li> </ul> COCO annotations of Beroe sp. B for the following 2 images: MCMEC2019_20191115_SONY_Beroe_sp_B_a_1 and MCMEC2019_20191115_SONY_Beroe_sp_B_a_2 <ul> <li>Callianira_cristata_images-coco 1.0.zip </li> </ul> COCO annotations of Callianira cristata for the following 21 images: MCMEC2019_20191120_SONY_Callianira_cristata_b_1 to MCMEC2019_20191120_SONY_Callianira_cristata_b_21 <ul> <li>Diplulmaris_antarctica_images-coco 1.0.zip</li> </ul> COCO annotations of Diplulmaris antarctica for the following 83 images: MCMEC2019_20191116_SONY_Diplulmaris_antarctica_a_1 to MCMEC2019_20191116_SONY_Diplulmaris_antarctica_a_9, and MCMEC2019_20191201_SONY_Diplulmaris_antarctica_c_1 to MCMEC2019_20191201_SONY_Diplulmaris_antarctica_c_74 <ul> <li>Koellikerina_maasi_images-coco 1.0.zip </li> </ul> COCO annotations of Koellikerina maasi for the following 49 images: MCMEC2018_20181127_NIKON_Koellikerina_maasi_b_1 to MCMEC2018_20181127_NIKON_Koellikerina_maasi_b_4, MCMEC2018_20181129_NIKON_Koellikerina_maasi_c_1 to MCMEC2018_20181129_NIKON_Koellikerina_maasi_c_29, and MCMEC2019_20191126_SONY_Koellikerina_maasi_a_1 to MCMEC2019_20191126_SONY_Koellikerina_maasi_a_16 <ul> <li>Leptomedusa_sp_A-coco 1.0.zip</li> </ul> COCO annotations of Leptomedusa sp. A for Figure 5 (see paper). <ul> <li>Leuckartiara_brownei_images-coco 1.0.zip</li> </ul> COCO annotations of Leuckartiara brownei for the following 48 images:  MCMEC2018_20181129_NIKON_Leuckartiara_brownei_b_1 to MCMEC2018_20181129_NIKON_Leuckartiara_brownei_b_27, MCMEC2018_20181129_NIKON_Leuckartiara_brownei_c_1 to MCMEC2018_20181129_NIKON_Leuckartiara_brownei_c_6, and MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_1 to MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_15 <ul> <li>MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_3-coco 1.0.zip</li> </ul> COCO annotations of Mertensiidae sp. A for the following video (total of 1847 frames): MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_3 (<a href="https://youtu.be/0W2HHLW71Pw">https://youtu.be/0W2HHLW71Pw</a>) <ul> <li>MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_3-coco 1.0.zip</li> </ul> COCO annotations of Leuckartiara brownei for the following video (total of 1367 frames): MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_3 (<a href="https://youtu.be/dEIbVYlF_TQ">https://youtu.be/dEIbVYlF_TQ</a>) <ul> <li>MCMEC2019_20191122_SONY_Callianira_cristata_a_1-coco 1.0.zip</li> </ul> COCO annotations of Callianira cristata for the following video (total of 2423 frames): MCMEC2019_20191122_SONY_Callianira_cristata_a_1 (<a href="https://youtu.be/30g9CvYh5JE">https://youtu.be/30g9CvYh5JE</a>) <ul> <li>MCMEC2019_20191122_SONY_Leptomedusa_sp_B_a_1-coco 1.0.zip</li> </ul> COCO annotations of Leptomedusa sp. B for the following video (total of 1164 frames): MCMEC2019_20191122_SONY_Leptomedusa_sp_B_a_1 (<a href="https://youtu.be/hrufuPQ7F8U">https://youtu.be/hrufuPQ7F8U</a>) <ul> <li>MCMEC2019_20191126_SONY_Koellikerina_maasi_a_1-coco 1.0.zip</li> </ul> COCO annotations of Koellikerina maasi for the following video (total of 1643 frames): MCMEC2019_20191126_SONY_Koellikerina_maasi_a_1 (<a href="https://youtu.be/QiBPf_HYrQ8">https://youtu.be/QiBPf_HYrQ8</a>) <ul> <li>MCMEC2019_20191129_SONY_Mertensiidae_sp_A_b_1-coco 1.0.zip</li> </ul> COCO annotations of Mertensiidae sp. A for the following video (total of 239 frames): MCMEC2019_20191129_SONY_Mertensiidae_sp_A_b_1 (<a href="https://youtu.be/pvXYlQGZIVg">https://youtu.be/pvXYlQGZIVg</a>) <ul> <li>MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_2-coco 1.0.zip</li> </ul> COCO annotations of Pyrostephos vanhoeffeni for the following video (total of 444 frames): MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_2 (<a href="https://youtu.be/2rrQCybEg0Q">https://youtu.be/2rrQCybEg0Q</a>) <ul> <li>MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_3-coco 1.0.zip</li> </ul> COCO annotations of Pyrostephos vanhoeffeni for the following video (total of 683 frames): MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_3 (<a href="https://youtu.be/G9tev_gdUvQ">https://youtu.be/G9tev_gdUvQ</a>) <ul> <li>MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_4-coco 1.0.zip</li> </ul> COCO annotations of Pyrostephos vanhoeffeni for the following video (total of 1127 frames): MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_4 (<a href="https://youtu.be/NfJjKBRh5Hs">https://youtu.be/NfJjKBRh5Hs</a>) <ul> <li>MCMEC2019_20191130_SONY_Beroe_sp_A_b_1-coco 1.0.zip</li> </ul> COCO annotations of Beroe sp. A for the following video (total of 2171 frames): MCMEC2019_20191130_SONY_Beroe_sp_A_b_1 (<a href="https://youtu.be/kGBUQ7ZtH9U">https://youtu.be/kGBUQ7ZtH9U</a>) <ul> <li>MCMEC2019_20191130_SONY_Beroe_sp_A_b_2-coco 1.0.zip</li> </ul> COCO annotations of Beroe sp. A for the following video (total of 359 frames): MCMEC2019_20191130_SONY_Beroe_sp_A_b_2 (<a href="https://youtu.be/Vbl_KEmPNmU">https://youtu.be/Vbl_KEmPNmU</a>) <ul> <li>Mertensiidae_sp_A_images-coco 1.0.zip</li> </ul> COCO annotations of Mertensiidae sp. A for the following 49 images: MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_c_1 to MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_c_2, MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_f_1 to MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_f_8, MCMEC2018_20181129_NIKON_Mertensiidae_sp_A_d_1 to MCMEC2018_20181129_NIKON_Mertensiidae_sp_A_d_13, MCMEC2018_20181201_ROV_Mertensiidae_sp_A_e_1 to MCMEC2018_20181201_ROV_Mertensiidae_sp_A_e_15, and MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_1 to MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_11 <ul> <li>Pyrostephos_vanhoeffeni_images-coco 1.0.zip</li> </ul> COCO annotations of Pyrostephos vanhoeffeni  for the following 14 images: MCMEC2019_20191125_SONY_Pyrostephos_vanhoeffeni_a_1 to MCMEC2019_20191125_SONY_Pyrostephos_vanhoeffeni_a_8, MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_1 to MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_6 <ul> <li>Solmundella_bitentaculata_images-coco 1.0.zip </li> </ul> COCO annotations of Solmundella bitentaculata for the following 13 images: MCMEC2018_20181127_NIKON_Solmundella_bitentaculata_a_1 to MCMEC2018_20181127_NIKON_Solmundella_bitentaculata_a_13

opencc-by-nc-sa-4.0Aug 2021View details →

Handwritten Text Recognition Ground Truth Set: StABS Ratsbücher O10, Urfehdenbuch X

Ground Truth for "Urfehdenbuch X der Stadt Basel (1563-1569)" at Staatsarchiv Basel-Stadt (StABS). Images and text aligned, using text-to-image (provided within Transkribus, <a href="https://www.readcoop.eu">www.readcoop.eu</a>). ALTO and Page XML are available for the text alignment. TEI to txt on page basis by Peter Dängeli. Derived from Transcription/TEI file: Urfehdenbuch X der Stadt Basel (1563-1569), in: Die Urfehdebücher der Stadt Basel – digitale Edition, hg. v. Susanna Burghartz, Sonia Calvi und Georg Vogeler Basel/Graz 2016. (zuletzt verändert am 31.1.2017): <a href="http://hdl.handle.net/11471/1010.2.1">hdl:11471/1010.2.1</a>. Image source: http://dokumente.stabs.ch/view/2010/Ratsbuecher_O_10/ CC-BY-NC-SA: The license is inherited from the project "Urfehdebücher der Stadt Basel – digitale Edition": http://gams.uni-graz.at/o:ufbas.1563.

Daily water quality data sets of the nine U.S. watersheds

US_daily_WQ_datasets_2.xlsx contains daily water quality (WQ) and discharge data from the nine watersheds in the United States. The data value of -999 means missing observation. The names (USGS station numbers) of nine WQ monitoring sites are Blanchard River (04189000), Cuyahoga River (0428000), Great Miami River (03271500), Honey Creek (04197100), Maumee River (04193500), Muskingum River (03150000), Portage River (04195500), Rock Creek (04197170), and Tiffin River (04185000). Daily WQ data were composed by following the procedure described in the Appendix S1 in Hirsch (2014). The all WQ data river were retrieved from the Heidelberg University's National Center for Water Quality Research site (https://ncwqr.org/monitoring/data) and discharge data were downloaded from the USGS National Water Information System (<a href="http://waterdata.usgs.gov/nwis/">http://waterdata.usgs.gov/nwis/</a> or https://doi.org/10.5066/F7P55KJN) in 2020. All the daily discharge data were acquired via the USGS National Water Information System. These data were used to evaluate the performance of the unbiased load estimates and confidence intervals of river loads based on the rating curve method using rejection sampling in the listed article below. To maintain the traceability of the proposed load estimation method and replicability of the results in the article, the authors of the article upload the data used in this repository.

opencc-by-4.0Jul 2020View details →

Phenomenological EOS Data Set

The MATLAB workspace file eos_data_set.mat contains the parameters and physical properties of 1,966,225 phenomenological neutron star (NS) equations of state (EOS).

Quantum coherent spin-electric control in a molecular nanomagnet at clock transitions. Open data set

Data supporting the related publication.

opencc-by-4.0Mar 2020View details →

Code and data sets for "DeepLC can predict retention times for peptides that carry as-yet unseen modifications"

Code used to prepare the data sets, calibrate retention times, generate DeepLC models, make predictions, and generate the figures. See README.md for more information on how to use these files and reproduce the results reported in the manuscript titled "DeepLC can predict retention times for peptides that carry as-yet unseen modifications".

Reference-Guided Scaffolding of Worldwide Fraxinus Species, Set 1

Following the recent assembly of the chromosome-level Fraxinus pennsylvanica genome, we used the assembly to construct reference-guided assemblies of 27 previously sequenced Fraxinus taxa, including Fraxinus excelsior. Thus we present a significant step forward in genomic resources for research and protection of Fraxinus species.

data set for 10.1109/TMAG.2021.3084866

Data set and python files for Fig.2, 3, 5 and 6 of 10.1109/TMAG.2021.3084866

opencc-by-4.0Apr 2006View details →

Figure 1. A in ''Riding'' behaviour by males of Conops quadrifasciata (Diptera: Conopidae): Do females set up ''riders'' as targets for takeovers by larger males?

Figure 1. A male rider employs an abdomen lift to thwart a slightly larger attacker.

Results files for "End-to-end Bayesian analysis for summarizing sets of radiocarbon dates"

These are the results files for the following peer-reviewed article: Price, M.H., J.M. Capriles, J. Hoggarth, R.K. Bocinsky, C.E. Ebert, and J.H. Jones, (2021). End-to-end Bayesian analysis for summarizing sets of radiocarbon dates. Journal of Archaeological Science. They were generated inside a Docker container as outlined in the README of this github repository: https://github.com/MichaelHoltonPrice/price_et_al_tikal_rc The analyses rely on an R package located in this github repository: https://github.com/eehh-stanford/baydem For the results archived here, the commits for each repository are: <pre>price_et_al_tikal_rc 3ac1e35f4277ef878f8e3aac3d05159928a09a2b baydem 1220a60a860633b51f9f07cbff3eb78f458efc1a</pre>

opencc-zeroAug 2021View details →

dryad36/100

Data from: Wild Goffin's cockatoos flexibly manufacture and use tool sets

The use of different tools to achieve a single goal is considered unique to human and primate technology. To unravel the origins of such complex behaviors, it is crucial to investigate tool use that does not occur species wide. These cases can be assumed to have emerged innovatively and be applied flexibly, thus emphasizing creativity and intelligence. However, it is intrinsically challenging to record tool innovations in natural settings that do not occur species-wide. Here we report the discovery of two distinct tool manufacture methods and the use of tool sets in wild Goffin's cockatoos (Cacatua goffiniana). Up to three types of wooden tools, differing in their physical properties and each serving a different function, were manufactured and employed to extract embedded seed matter of Cerbera manghas. While Goffin's cockatoos do not depend on tool-obtained resources, repeated observations of two temporarily kept wild birds and indications from free-ranging individuals suggest this behavior occurs in the wild, albeit not species-wide. The use of a tool set in a non-primate implies convergent evolution of advanced tool use. Furthermore, these observations demonstrate how a species without hands can achieve dexterity in a high-precision task. This finding of flexible use and manufacture of tool sets in animals distantly related to humans significantly diversifies the phylogenetic landscape of technology.

opencc-by-4.0Sep 2021View details →

iPPG2cPPG: excerpts taken from the test set

Excerpts taken from the test set. Use them to test the neural networks described in the orginal article (available <a href="https://zenodo.org/record/5482374">here</a>).

opencc-by-4.0Sep 2021View details →

data set regarding to project- Malnutrition, sarcopenia and malnutrition-sarcopenia syndrome in Older Adults with COPD

data set regarding to project- Malnutrition, sarcopenia and malnutrition-sarcopenia syndrome in Older Adults with COPD

opencc-by-4.0Sep 2021View details →

Shared motivations, goals and values in the practice of personal science - Qualitative data set

269 transcribed excerpts coded from 22 interviews to self-researchers for the study "Shared motivations, goals and values in the practice of personal science - A community perspective on self-tracking for empirical knowledge". Interviews with participants were conducted via video conferencing and were based on a list of open-ended questions, separated into key sections around participation and collaboration in personal science. Participants who agreed to be interviewed, gave informed consent in like with the ethics approval by the Inserm Institutional Review Board (IRB) for this study, and regarding this data set, previous agreement in compliance with privacy and anonymity requirements. Academic article based on this dataset: Senabre Hidalgo, E., Ball, M. P., Opoix, M., & Greshake Tzovaras, B. (2022). Shared motivations, goals and values in the practice of personal science: a community perspective on self-tracking for empirical knowledge. Humanities and Social Sciences Communications, 9(1), 1-12. <a href="https://doi.org/10.1057/s41599-022-01199-0">https://doi.org/10.1057/s41599-022-01199-0</a>