Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.

This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps. 1.     Seismicity catalogues The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.  Dataset list: <ul> <li>Cahuilla Catalogues (modified from Ross et al., 2019): </li> <ul> <li>Original data: File name: VR_sup_0021_Cah_All</li> <li>Filtered data: File name: VR_sup_0022_Cah_Filter</li> </ul> <li>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </li> <ul> <li>Original data: File name: VR_sup_0023_Boh_08_All</li> <li>Filtered data: File name: VR_sup_0024_Boh_08_Filter</li> </ul> <li>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </li> <ul> <li>Original data: File name: VR_sup_0025_Boh_14_All</li> <li>Filtered data: File name: VR_sup_0026_Boh_14_Filter</li> </ul> <li>Yellowstone Catalogs (modified from Shelly et al., 2013): </li> <ul> <li>Original data: File name: VR_sup_0027_Yell_14_All</li> <li>Filtered data: File name: VR_sup_0028_Yell_14_Filter</li> </ul> </ul> The files are text files tab-delimited, with the following headers: <ul> <li>Index: 1 by default</li> <li>Easting(m): hypocenter Easting in meters </li> <li>Northing(m): hypocenter Northing in meters </li> <li>Depth(m): hypocenter depth in meters </li> <li>Mw: magnitude</li> <li>Relative Time(s): date of the origin time in the format </li> </ul> 2.     Seismicity catalogues The pictures series are images of seismicity at a regular time interval for each studied step. Dataset list: <ul> <li>Step C1: File name: VR-sup-0012-Pictures_C1.</li> <li>Step C2: File name: VR-sup-0013-Pictures_C2.</li> <li>Step C3: File name: VR-sup-0014-Pictures_C3.</li> <li>Step C4: File name: VR-sup-0015-Pictures_C4.</li> <li>Step Y1: File name: VR-sup-0016-Pictures _Y1.</li> <li>Step B1I: File name: VR-sup-0017-Pictures _B1I.</li> <li>Step B1II: File name: VR-sup-0018-Pictures _B1II.</li> <li>Step B2: File name: VR-sup-0019-Pictures _B2.</li> <li>Step B3: File name: VR-sup-0020-Pictures _B3.</li> </ul> Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals. If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).

opencc-by-4.0Oct 2024View details →

East African topography and volcanism explained by a single, migrating plume: supplementary data

These data accompany the following paper: Hassan, R., Williams, S.E., Gurnis, M. and Müller, D., 2020. East African topography and volcanism explained by a single, migrating plume. Geoscience Frontiers, 11(5), pp.1669-1680. The data (in simple text form) correspond to the dynamic topography and change in dynamic topography shown in Figure 7.

opencc-by-4.0Feb 2023View details →

Migration Drivers Data Inventory Records

This inventory includes metadata on various quantitative sources of information on migration drivers that can be used for modelling purposes. Additionally, the inventory includes information on articles that used those quantitative sources such as the statistical effect found in their analysis.

opencc-by-4.0Jul 2023View details →

Data for: World's human migration patterns in 2000-2019 unveiled by high-resolution data

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.  Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: <a href="https://doi.org/10.1038/s41562-023-01689-4" target="_blank" rel="noopener">https://doi.org/10.1038/s41562-023-01689-4</a>  You can explore the data in our online net-migration explorer: <a href="https://wdrg.aalto.fi/global-net-migration-explorer/" target="_blank" rel="noopener">https://wdrg.aalto.fi/global-net-migration-explorer/</a>   Short introduction to the data For the dataset, we collected, gap-filled, and harmonised:   <ol> <li>a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and  </li> <li>sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).</li> </ol> These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).   We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.  Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.    List of datasets Birth and death rates:  raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)  raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)  tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)  tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)    Net-migration:   raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)  raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)  raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)  raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)    polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)   polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)  polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)    Files to run online net migration explorer  masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (<a href="https://wdrg.aalto.fi/global-net-migration-explorer/">https://wdrg.aalto.fi/global-net-migration-explorer/</a>). The source code of this application is available in <a href="https://github.com/vvirkki/net-migration-explorer">https://github.com/vvirkki/net-migration-explorer</a>. Running the application locally requires these two .rds files from this repository.    Metadata  Grids:  Resolution: 5 arc-min (0.083333333 degrees)  Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)  Coordinate ref system: EPSG:4326 - WGS 84  Format: Multiband geotiff; each band for each year over 2000-2019  Units:  <ul> <li> Birth and death rates: births/deaths per 1000 people per year  </li> <li> Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)  </li> </ul>   Geospatial polygon (gpkg) files:  Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)  Temporal extent: annual over 2000-2019  Coordinate ref system: EPSG:4326 - WGS 84  Format: gkpk  Units:   <ul> <li> Net-migration: persons per 1000 people per year  </li> </ul>

opencc-by-4.0Nov 2020View details →

Data for: "Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA)"

This dataset contains the spring and autumn migration phenology dataset used in Haest et al. (2020) to determine the drivers of migration phenology of Brazilian free-tailed bats at Bracken Cave (USA) over the period 1995-2017. The phenology dataset was derived from nightly colony population sizes estimated using weather radar data (Stepanian et al., 2018). See the Materials and Methods section in Haest et al. (2020) for more details on the dataset.  References: Haest, B., Stepanian, P. M., Wainwright, C. E., Liechti, F., & Bauer, S. (2021). Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA). Global Change Biology, 27(4), 768-780. <a href="https://doi.org/10.1111/gcb.15433">https://doi.org/10.1111/gcb.15433</a> Stepanian, P. M., & Wainwright, C. E. (2018). Ongoing changes in migration phenology and winter residency at Bracken Bat Cave. Global Change Biology, 24(7), 3266–3275. <a href="https://doi.org/10.1111/gcb.14051">https://doi.org/10.1111/gcb.14051</a>

opencc-by-4.0Jun 2021View details →

Data from: Advancement in long-distance bird migration through individual plasticity in departure

Research summary: Globally, bird migration is occurring earlier, consistent with climate-related changes in breeding resources. Although often attributed to phenotypic plasticity, there is no clear demonstration of long-term population advancement in avian migration through individual plasticity. Using direct observations of bar-tailed godwits (Limosa lapponica) departing New Zealand on a 16,000-km journey to Alaska, we show that migration advanced by six days during 2008–2020, and that within-individual advancement was sufficient to explain this population-level change. However, in individuals tracked for the entire migration, earlier departure did not lead to earlier arrival or breeding in Alaska, due to prolonged stopovers in Asia. Moreover, changes in breeding-site phenology varied across Alaska, but were not reflected in within-population differences in advancement of migratory departure. We demonstrate that plastic responses can drive population-level changes in timing of long-distance migration, but also that behavioral and environmental constraints en route may yet limit adaptive responses to global change. The collection of long-term departure data was supported by Chris & Neville Hopkins, David & Lucile Packard Foundation, Dobberke Foundation for Comparative Psychology, Manawatu Estuary Trust, Marsden Fund (Royal Society of New Zealand), Massey University Doctoral Scholarship, New Zealand Department of Conservation, Ornithological Society of New Zealand, Pacific Shorebird Migration Project, Pūkorokoro Miranda Naturalist’s Trust, and Royal Netherlands Academy of Arts & Sciences.

opencc-by-4.0Apr 2022View details →

Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?

Abstract Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins Erithacus rubecula ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.

opencc-by-4.0Oct 2022View details →

Data from: Male long-distance migrant turned sedentary; The West European pond bat (Myotis dasycneme) alters their migration and hibernation behaviour

Winter survey data, temperature data and mark recapture data of Myotis dasycneme. This study aimed to better understand the migration, mating and hibernation choices of the pond bat.   The study area covered the whole of the Netherlands, Belgium and East Frisia (northwest Germany). We defined two study periods, data collected between 1930 and 1980 (Sluiter and van Heerdt) and data between 1980 and 2015 (Haarsma). All available mark and recovery data (ringing) of both the historical and recent migration research were digitized. Observations include location and date of capture, species, sex and ring number. The latest observations in the recent dataset (Haarsma) also include biometric measurements (forearm length, body mass) and information about age and reproductive status. These biometric measurements show that male pond bats are on average smaller and lighter than females (body mass (g)/ forearm length (mm) females: 18.9/47.1, males: 16.4/46.4). The dataset shows changes in the fat mass of both sexes during a year. This study also compares migration data with winter monitoring survey data. We selected winter roosts with three or more records of three or more pond bats in one or both of the study periods. Only data from sites with long-term data series (from the hibernacula in the Dutch provinces of Zuid-Holland, Gelderland and Limburg) were used to analyse trends and annual abundance. Our selection included 59 limestone mines in the province of Limburg and 16 WOII bunkers in Gelderland and 38 in Zuid-Holland. We divided the sites into 'core' and 'satellite' sites depending on the timing of first colonization.   Bunker limestone mine microclimate   Radiation temperature: radiation temperature of the wall, measured with a non-contact infrared thermometer How many bats: the group size of each bat/ group of bats observed, categorized as alone and group. Where: the hanging location of the observed bat, categorized as hidden (in crevice) or free (free on ceiling or wall) Date: date of the observation Xy-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service. Type: Bunker or limestone Location description: description of the name of the site   Bunker monitoring core and satellite   Date: date Winter: the period between September and April is defined as the winter of the year starting in January. Location description: description of the name of the site N of pond bats: total number of observed pond bats Province: the province Type: hibernacula categorized as a core or satellite site, sites occupied by pond bats since 1977 and 1997 respectively. XY-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.     Supporting information (as referenced in the published paper, hence also available with plos one) S1 Fig. The range of the West European pond bat population (TIF). The shaded areas indicate the areas where the bulk of the surveys were carried out. S2 Fig. The distribution of the pond bat in Europe (country boundaries are only indicative) (JPG). Within the whole range of the species distribution seven groups can be separated. A The Netherlands, Belgium and Northwest Germany (~the West European population), B Jutland Peninsula, C Central European lakelands, D The Baltic States, E Ural Mountains (hibernacula), F Volga Valley (summer nurseries), G Hungary and Romania. S3 Fig. The distribution of hibernacula used by the western pond bat population (TIF). These are sites with three or more records of pond bats in one or both study periods. We identified four roost categories: Roosts which have been used ever since 1900 (= green squares), roosts used only between 1900–1980 (= open black squares), roosts occupied after 1980 (= purple circles), roosts occupied after 1997 (= blue asterisks). Detailed maps, all with the same enlargement, of the clusters in the provinces of Zuid-Holland (1), Gelderland (1) and Limburg (3) are provided.   S1 Table. Summary of the average weight of pond bats over the study period. The weight is averaged per week. The table gives average weight of females, males both adults and juveniles.   Avg weight: average weight of pond bats of each sex, in a certain week Sex: male of female Week number: number of the week Age: juvenile (or young of the year). Defined as the from birth until the onset of first hibernation. Subadult or sexual immature, defined as individuals with no signs of (past) reproductive activity. Adult or sexual mature, defined as all individuals with signs of  (previous) reproductive activity. N observations: number of observations within each subset.   S2 Table. Mark and recapture data from the historical dataset.   Ringnumber: the label of the ring  Sex: male or female capture date: date of capture capture location: description of capture location x y coordinate: The coordinates of the capture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service. recapture date: date of recapture recapture location: description of recapture location x y coordinate: The coordinates of the recapture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.   S3 Table. Mark and recapture data from the recent dataset.   Same dataset as the historical set, but now including age (see definition used in S1)

opencc-by-4.0Jan 2019View details →

MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015

In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. We present a dataset of 3275 Tweets form the months September and October 2015. These Tweets are annotated regarding their relevance to the quantitative movement of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for an early warning or forecasting system

opencc-by-4.0Jul 2021View details →

Data on the sex and age composition of Bramblings Fringilla montifringilla during autumn migration and winter in Europe

Abstract Bramblings Fringilla montifringilla are known to vary in winter distribution according to sex and age (differential migration). This pattern is complicated by the large yearly fluctuation in their preferred winter food, beech seeds. In beechmast areas, large concentrations of Bramblings occur, and their sex and age composition differs from that of winters without a beechmast. Here we present data on the sex and age composition of Bramblings during autumn migration and winters with and without beechmast, mainly from Switzerland, supplemented with data from northern and southern Europe.   Literature cited in the Excel-file Arizaga J, Zuberogoitia I, Zabala J, Crespo A, Iraeta A, Belamendia G (2012) Seasonal patterns of age and sex ratios, morphology and body mass of Bramblings Fringilla montifringilla at a large winter roost in southern Europe. Ring. Migr. 27:1–6. https://doi.org/10.1080/03078698.2012.686707 Browne SJ, Mead CJ (2003) Age and sex composition, biometrics, site fidelity and origin of Brambling Fringilla montifringilla wintering in Norfolk, England. Ring. Migr. 21:145–153. https://doi.org/10.1080/03078698.2003.9674283 Khil L, Samwald O, Tiefenbach A, Tiefenbach M, Pacher H (2011) Der Massenschlafplatz von Bergfinken Fringilla montifringilla in Österreich im Winter 2008/2009. Limicola 25:81–100 Kjellén N, Lindström Å (1993) Bergfinkens övervintringsstrategier samt några iakttagelser från en skånsk sovplats i januari-februari 1993. Anser 32:187–199 Robson D (1996) Influencia de la temperatura en la masa corporal del Pinzon Real. Ardeola 43:139–144 Schierer A (1957) Geschlechts- und Altersverhältnis der Nordfinken. Vögel Heimat 27:68 Widemo U (1977) Bergfink Fringilla montifringilla Jan. - Apr. 1977. Sex and age distribution, winglength and change of weight. Fåglar i Sörmland 10:76–81

opencc-by-4.0Sep 2021View details →

Migrating Tides in the Stratosphere from COSMIC Radio Occultation Data

These analyses of the migrating tides in temperature, microwave refractivity, and geopotential in the Earth’s stratosphere are analyzed using GPS radio occultation (RO) data obtained by the COSMIC constellation of six satellites in orbit planes separated by 30° in ascending node. The tides are analyzed monthly, beginning with November 2006 and ending with December 2016. The radio occultation retrievals used as input to the analyses are obtained from the Climate Data Record v1 of the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF; romsaf.org). The reference model that is used for the sake of comparison are the 3-, 6-, 9-, and 12-hr forecasts of the ERA-Interim reanalysis project.

opencc-by-4.0Nov 2022View details →

Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy (Data and Associated Files for Dissertation)

This publication contains files associated with analysis for my dissertation, "Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy." The dissertation explores a potential relationship between political violence and a state leader's choice to use migration as a bargaining chip in pursuit of foreign policy objectives. "Key to datasets.docx" and "dataframes_viz.png" explain the contents of the five datasets used. These five datasets are the five .dta files. There are five log files (.txt) and five do files containing code (.do) corresponding to the five datasets. Finally, each dataset has three associated results tables (.xls) for a total of fifteen .xls files.

opencc-by-4.0Feb 2023View details →

Migration Data Inventory Records

This inventory includes metadata on various quantitative sources of information on migration that can be used for modelling purposes. Documentation: http://www.quantmig.eu/res/QuantMig_documentation_website.pdf Instructional video: https://www.youtube.com/watch?v=Lqm36f0lXho How to cite: Soto Nishimura A and Mooyaart J (2021) Migration Data Inventory Records: Data Inventory. Online resource, available at <a href="http://www.quantmig.eu/data_inventory/">http://www.quantmig.eu/data_inventory/.</a>

Data_Figure 2_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 2 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1007_s00018-019-03227-w_CMLS_Fig2). Corresponding raw data obtained from a) Migration potential as four files in CSV format (31003A-179400_ date_examiner_17BHSD12_16_1_1-4. b) mRNA content analyzed by RT-PCR provided as ten files in CSV format (31003A-179400_date_examiner_17BHSD12_1_1-2_1-6) and proliferation investigation on xCELLigence provided as six files in CSV format (31003A-179400_date_examiner_17BHSD12_9_2_1-6). All further experiment related information and subsequent data analysis provided as four meta-data-files (31003A-179400_ date_examiner_17BHSD12_16/1/9_dataset_M_1) as TXT format.

Data_Figure 6_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 6 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 6). Corresponding raw data obtained from a1/2) cellomics HTC array scan analysis provided as six files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_6_1-6), b1/2) oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) provided as 10 files in CSV format (31003A-179400_20190521_MT_17BHSD12_10_3-4_1-5); c 1/2 ), cellomics HTC array scan analysis provided as 12 files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_7-8_1-8). d) Western blot and densitometry provided as eight files in CSV format (31003A-179400_Date_examiner_17BHSD12_2_3-4_1-5). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_8/10/2_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_Figure 7_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 7 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 7). Corresponding raw data obtained from a1/2) Western blot and densitometry provided as eight files in CSV format (31003A-179400_date_examiner_17BHSD12_2_5-6_1-6); b) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_6_1-4); c 1/2) cellomics HTC array scan analysis provided as 11 files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_9-10_1-6); d1/2); Western blot and densitometry provided as eight files in CSV format (31003A-179400_date_examiner_17BHSD12_2_7-8_1-6). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_2/1/8_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_Figure 5_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 5 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 5). Corresponding raw data obtained from oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) provided as 10 files in CSV format (31003A-179400_20190521_MT_17BHSD12_10_1-2_1-5). All further experiment related information and subsequent data analysis provided as two meta-data-file: (31003A-179400_20190521_MT_17BHSD12_10_1-2_1) as TXT format.

Data_Figure 4_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 4 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 4). Corresponding raw data obtained from: a1/2) Migration potential as five files in CSV format (31003A-179400_date_examiner_17BHSD12_16_3_1-5); b 1/2) Migration potential as four files in CSV format (31003A-179400_date_examiner_17BHSD12_16_4_1-4); c1/2/3) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_4_1-4); cellomics HTC array scan analysis provided as three files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_3-4_1-4); d) Migration potential as five files in CSV format (31003A-179400_ date_examiner_17BHSD12_16_5_1-5); e) mRNA content analyzed by RT-PCR provided as six files in CSV format (31003A-179400_date_examiner_17BHSD12_1_5_1-6); f) Migration potential as four files in CSV format (31003A-179400_date_examiner_17BHSD12_16_6_1-4), cellomics HTC array scan analysis provided as three files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_5_1-5); g) ELISA measurement provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_20_1_1-4). All further experiment related information protocols and subsequent data analysis provided as 10 meta-data-files (31003A-179400_date_examiner_17BHSD12_8/16/1/20_dataset_M_1) as TXT format.