Volume 1 - 2020 | https://doi.org/10.3389/frvir.2020.611091 Virtual reality (VR) technology provides clinicians and researchers with new opportunities to observe and train behavior in realistic yet well-controlled environments compared to more abstract experiments and tests on 2D computer screens which can make it more expensive and time-consuming One way to overcome these challenges is to create and validate VR content and to make it openly available for researchers and clinicians Here we introduce the OpenVirtualObjects (OVO) a set of 124 realistic 3D household objects that people encounter and use in their everyday lives The objects were rated by 34 younger and 25 older adults for recognizability All participants also named and categorized the objects We provide the data and the experiment- and analysis code online we hope to facilitate VR-based research and clinical applications Easy and free availability of standardized and validated 3D objects can support systematic VR-based studies and the development of VR-based diagnostics and therapeutic tools Virtual reality (VR) technology provides computer-generated content based on a user's movement in or interaction with a simulated environment to enable an immersive experience (Aukstakalnis and Blatner, 1992; Rizzo and Koenig, 2017) users interact with 3D computer graphics on a 2D screen people are fully “embedded” in the virtual environment through the use of stereoscopic head-mounted displays (HMDs) and body-tracking sensors because the therapist has to be present at all times and the real-life environment has to be physically built or made available they often lack the experimental control necessary for systematic and reproducible results and they often do not allow for automatic (i.e. experimenter-independent) multi-dimensional data collection To further advance the standardization of VR-based experiments and applications (e.g. for diagnostics and training) and to reduce the costs of VR-based research OVO is a freely available set of 124 realistic 3D household objects for VR-based research and clinical applications (e.g. VR provides a unique opportunity to create assessments and trainings that are relevant to the real environments of people while still maintaining full control over the stimuli (unlike in the real world) we selected objects that naturally occur in a household setting This could increase the amount of transfer from training to real life use of certain skills and it could provide a more realistic assessment of cognitive abilities The objects in the database were rated by younger and older adults on the dimensions of recognizability Participants also categorized and named the objects table) normed by younger adults for name agreement Names and lexical characteristics of the names are also described The OVO objects complement these data sets because they were specifically selected and rated for their appearance in everyday life (i.e. and they were rated on these properties by both younger and older adults The selection of objects in OVO aimed to increase the personal relevance of the objects—to maximize the link between the VR-based experiments or applications and everyday life Our goal is to provide clinical and fundamental research community with a validated resource for their own VR-based applications as well as their intended sample Thirty-four younger (19 females; mean age: 28 ± 4.6; range 20–38 years) and 25 older adults (14 females; mean age: 70 ± 5.0; range 62–82 years) participated in the study The data from 3 subjects was discarded due to red-green blindness All participants were native speakers of German and had normal or corrected-to-normal vision They provided written informed consent and were paid for participation (9 €/h) Ethical approval was granted by the ethics committee of the Psychology Dept at the Humboldt-Universität zu Berlin Figure 1. Nine example 3D household objects from the freely available OpenVirtualObjects (OVO). (https://edmond.mpdl.mpg.de/imeji/collection/7L7t07UXD8asG_MI) The experiment was created in Unity version 2017.4.5 (www.unity.com) and run under Ubuntu 18.04 on desktop computers Participants were seated 55 cm in front of a 22-inch computer screen (EIZO S2202W) with a 1,680 × 1,050-pixel resolution The Unity code for the rating experiment is available in the OVO database to facilitate the collection of additional ratings or to run rating studies for new objects All ratings and names were collected in one experimental session participant provided informed written consent Participants were instructed that they would see several objects The experiment consisted of five “scenes,” or phases 1) A randomly selected object rotated on a table around its central vertical axis with a speed of 60° per second for 8 s how well they recognized the object (1 = not well; 100 = very well) Then they typed the name of the object and indicated how certain they were of the name (1 = not certain they typed “11” and if they did not recognize the object they typed “00” for the name of the object and the certainty ratings were ignored 3) Participants selected the most appropriate category for the object from ten categories: clothes; cosmetics; cutlery and dishes; decoration; food; office supplies; tools; toys; kitchen utensils; unknown They also rated how well the object fit the chosen category (1 = not at all; 100 = very well) 4) Participants rated how familiar the object was to them (1 = not familiar; 100 = very familiar) and how detailed (visually complex) it appeared to them (1 = not very detailed; 100 = very detailed) 5) Participants rated how often they encounter the object at home (“contact”) and how often they use the object at home (i.e. also on scales from 1 (never) to 100 (very often) participants were presented with five practice scenes in which one object (a pineapple) was rated The objects were presented in four blocks of 31 objects each participants were asked to take a break for 1 to 5 min Apart from the object in the practice trial object presentation was randomized using the “Random.Range” function in Unity so that numbers (corresponding to individual objects) were chosen with equal probability from a finite set of numbers and maximum were calculated for each dimension (Recognizability Normality was tested with Shapiro–Wilk tests Non-parametric (Spearman's rank) correlations were used to calculate correlation coefficients between the dimensions Adjustment for multiple comparisons was implemented using Holm's method For the naming data, we calculated the name agreement (NA, in %) and the H-statistic (Snodgrass and Vanderwart, 1980) The two categories of naming failures (“11” if they did not know the name “00” if they did not recognize the object) were excluded from the analysis Misspellings were included as the correctly spelled name and elaborations [e.g. halbes Brot (“half bread”) Haarkamm (“hairbrush”)] were counted as separate names If participants wrote down two distinct names for an object The NA is the percentage of people that produced the modal name (i.e. the name given by the majority of participants) which considers the frequency distribution of the given names as well as number of alternative names An object that was given the same name from every participant in the sample would have an H-value of 0.00 An object that elicited two different names with exactly the same frequency would have an H-value of 1.00 we additionally calculated the modal name per object and then grouped all words that literally contained the modal name before recalculating the H-statistic and the NA we categorized the object based on which category was most often chosen by the participants (for the younger and older adults separately) Objects could only be assigned to one category We calculated the number of objects that were attributed to the given category and the percentage of agreement (i.e. the percentage of participants that chose the objects in the category as belonging to that category) if a given category has two objects A and B and for object A 90% of participants said the items belonged to this category and for object B 60% put the item in this category the average agreement for the category will be 75% which will increase the number of objects in that category (e.g. For comparison and to facilitate the pooling of objects, we provide the mean ratings across objects in our database together with those of two other 3D object (Peeters, 2018; Popic et al., 2020) and three colored-photograph databases (Adlington et al., 2009; Brodeur et al., 2010; Moreno-Martínez and Montoro, 2012) Common dimensions were the H-statistic (H) our objects were rated on a sliding scale from 1 to 100 and the other databases used a 5-point scale We acknowledge that rating on a scale from 1 to 100 is different from rating on a 5-point scale thus we only report these measures and do not perform any statistical analyses The summary statistics for the collected norms (Recognizability, Familiarity, Details, Contact, and Usage) are presented in Table 1 for the younger and older adults separately. The Shapiro-Wilk tests showed that the data were not normally distributed for the younger nor for the older adults (all p < 0.05). Figure 2 displays the ratings for each of the norms for the younger adults Boxplots with individual data points for the object ratings for the entire sample (red) and per age group (older: white To explore the relationships between the different dimensions, Spearman correlation analyses (Holm-corrected for multiple comparisons) were used (Table 2) there were significant correlations between Contact and Usage there was a positive correlation between Details and Recognizability for both age groups For the older but not for the younger adults Recognizability and Usage were significantly correlated Correlation matrix for the younger (upper panel) and older (lower panel) adults The percentage of “no recognition” responses (i.e. “I do not recognize the object,” coded as “00”) was 2.56 % for the younger and 2.47% for the older adults The amount of “no name” responses (i.e. “I do not know the name of the object,” coded as “11”) was 1.91% for the younger and 1.43% for the older adults The mean H-statistic for the younger adults was 1.86 (SD = 1.09) and the average NA was 59.36% (SD = 25.72%) the mean H-statistic was 2.12 (SD = 1.04) and the average NA was 52.02% (SD = 24.31%) and the percentage of “no recognition” (“00”) and “no name (“11”) responses per object can be found in the online database (for the complete sample and separately for younger and older adults) grouping together all names that contained the modal name the mean H-statistic was 1.28 (SD = 1.11) and the average NA was 72.76% (SD = 25.19%) for the younger adults the mean H-statistic was 1.60 (SD = 1.19) and the average NA was 63.98 % (SD = 27.64%) Table 3 shows the distribution of objects over the semantic categories “cosmetics” was the category with most objects and “toys” with the least objects for both age groups The categorization of objects in the “toys” and “food” categories was the most consistent the younger adults categorized more objects as “unknown” than the older adults Distribution of objects over the categories for younger and older adults what percentage of participants chose this category (i.e. the percentage of agreement for this category) We also provide data per object on how well the objects fit the chosen category in the database online (1 = not at all; 100 = very well) Table 4 presents the comparison of ratings to other databases. The mean familiarity for OVO was numerically comparable to the norms of Brodeur et al. (2010) and slightly higher than the other databases (i.e., Adlington et al., 2009; Moreno-Martínez and Montoro, 2012; Peeters, 2018; Popic et al., 2020) The average visual complexity as well as the H-statistic were higher for OVO compared to the other databases while the NA was lower Overview of the standardized measures from the 3D objects in OVO (complete sample) and comparable databases Recognizability ratings were generally high for both age groups suggesting that the objects can be recognized well by both age groups Familiarity ratings suggest that both groups were used to (most of) the objects The ratings for contact and usage showed a larger variance suggestion that OVO contains objects that people frequently encounter or use in their households and objects they do not encounter or use often we advise OVO users to select objects based on the ratings from the age group that best resembles their target population The correlation analysis revealed that especially the dimensions contact and usage are highly correlated for both samples This indicates that the items that participants often came into contact with or that these dimensions measure the same item property The positive correlation between details and recognizability suggests that objects with more details (i.e. higher visual complexity) were recognized better This suggests for VR-based studies that the quality of the stimulus in VR is related to the recognizability of the object for tasks that require the recognition of objects researchers should aim to use as high-quality representations of VR objects as possible (e.g. The category data revealed that most objects could be categorized within the pre-determined categories which covered large parts of the household (e.g. OVO users can pick and choose particular categories to use the objects with the highest norms relevant to their research goals the category “toys” is particular as it contains miniature versions of non-household objects (e.g. Outside the (virtual) household setting or context it might not be clear that these items are toys even though these were rated in immersive VR Researchers and clinicians should take the complexity ratings into account especially for studies in which the visual properties of the stimuli have to be strictly controlled Future studies will have to address the differences in visual complexity ratings between 2D and 3D objects and the properties that contribute to visual complexity of 3D objects The H-statistic was higher and the NA was lower for OVO than for the other databases suggesting less uniform naming behavior for the OVO objects The strong variance in these statistics across OVO objects could be because participants entered detailed descriptions with only fine-grained deviations [e.g. mechanischer Wecker (“mechanical alarm clock”) and Analogwecker (“analog alarm clock”)] To facilitate the comparison with the other databases we treated these elaborations as distinct names in our main analysis such strict conventions may not be necessary or appropriate for all studies using the OVO objects we grouped all names that literally contain the modal name (i.e. summarized all elaborations under the modal name) and found that the resulting H-statistics and NA were in the range of those reported for other databases For many studies such an account should be sufficient to ensure that an object was recognized as belonging to the concept described by the modal name For researchers that require exact naming distinctions and high levels of name agreement we suggest to filter the OVO database by defining a threshold for the H-statistic or the percentage of name agreement We provide data and R script templates to perform such operations household items are presented on a virtual table and the participant or patient is asked to memorize the location of the objects the objects disappear and the participants is asked to recall the location of each object The number of items to be memorized is gradually increased from four to seven and also perspective changes can occur during the task (i.e. the participant has to recall the object locations from a different side of the table) One of the important outcome measures is the difference between the actual location and the recall location (displacement error) If OVO objects were to be used in this type of study those with similar ratings) to reduce the influence of undesirable object characteristics such as familiarity visual complexity and name agreement on the memory process that is of interest Another possibility is to include the ratings in the statistical analyses to account for their influence on the outcome measure OVO provides a handle to select objects in advance or to account for differences in the perception and memory retrieval of objects and their names based on age differences We provide all scripts of the analyses described in this paper in the online database so that users can easily tailor the analyses to their needs and select the most appropriate set of objects or ratings we did not collect image agreement scores (i.e. scores of how well the object fits the modal name it was given) this was a pragmatic decision to reduce experiment time and these norms can be easily collected with the existing experiment code by providing the modal names collected in OVO together with the objects We hope that the objects in this database are useful in experimental and clinical settings—or in other situations that require standardized 3D stimuli We invite researchers to select objects according to their research questions and target populations and to add more objects or norm values to the database whenever possible we can increase the amount and quality of VR-based experimental research and clinical applications The datasets generated for this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://edmond.mpdl.mpg.de/imeji/collection/7L7t07UXD8asG_MI&lt The studies involving human participants were reviewed and approved by Psychology Department at the Humboldt-Universität The patients/participants provided their written informed consent to participate in this study LP and MA programmed and conducted the experiment All authors contributed to the article and approved the submitted version This project was funded by a grant (BMBF grant 13GW0206) from the German Federal Ministry for Education and Research for the research consortium VReha—Virtual worlds for digital diagnostics and cognitive rehabilitation The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest We would like to thank all members of the VReha team and Angelika Thöne-Otto for their valuable input during the meetings of the VReha–Virtual worlds for digital diagnostics and cognitive rehabilitation consortium The hatfield image test (HIT): a new picture test and norms for experimental and clinical use CrossRef Full Text | Google Scholar Silicon Mirage; The Art and Science of Virtual Reality Google Scholar Working memory is not fixed-capacity: More active storage capacity for real-world objects than for simple stimuli a new set of 480 normative photos of objects to be used as visual stimuli in cognitive research The interleaving of actions in everyday life multitasking demands Age-related differences in valence and arousal ratings of pictures from the International Affective Picture System (IAPS): do ratings become more extreme with age A meta-analysis and systematic literature review of virtual reality rehabilitation programs CrossRef Full Text | Google Scholar Why most published research findings are false PubMed Abstract | CrossRef Full Text | Google Scholar “Performance within the virtual action planning supermarket (VAP-S): An executive function profile of three different populations suffering from deficits in the central nervous system,” in 7th International Conference on Disability Virtual Reality and Associated Technologies with ArtAbilitation (Maia) “Using EEG to decode subjective levels of emotional arousal during an immersive VR roller coaster ride,” in 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (Reutlingen) Validity evaluation of a spatial memory task in virtual environments Google Scholar Multidimensional evaluation of virtual reality paradigms in clinical neuropsychology: application of the VR-check framework Moreno-Martínez An ecological alternative to snodgrass & vanderwart: 360 high quality colour images with norms for seven psycholinguistic variables Can virtual reality exposure therapy gains be generalized to real-life a meta-analysis of studies applying behavioral assessments Le test de copie d'une figure complexe; contribution à l'étude de la perception et de la mémoire [Test of copying a complex figure; contribution to the study of perception and memory] Google Scholar A standardized set of 3-D objects for virtual reality research and applications PubMed Abstract | CrossRef Full Text | Google Scholar Database of virtual objects to be used in psychological research L'examen psychologique dans les cas d'encéphalopathie traumatique [The psychological examination in cases of traumatic encepholopathy problems.] Google Scholar Development and early evaluation of the virtual Iraq/Afghanistan exposure therapy system for combat-related PTSD Is clinical virtual reality ready for primetime PubMed Abstract | CrossRef Full Text | Google Scholar False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant “BigBIRD: a large-scale 3D database of object instances,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) (Hong Kong) Virtual reality in the diagnostics and therapy of neurological diseases A standardized set of 260 pictures: Norms for name agreement The combined use of virtual reality and EEG to study language processing in naturalistic environments Villringer A and Gaebler M (2020) OpenVirtualObjects: An Open Set of Standardized and Validated 3D Household Objects for Virtual Reality-Based Research Received: 28 September 2020; Accepted: 23 November 2020; Published: 22 December 2020 Copyright © 2020 Tromp, Klotzsche, Krohn, Akbal, Pohl, Quinque, Belger, Villringer and Gaebler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) distribution or reproduction in other forums is permitted provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited in accordance with accepted academic practice distribution or reproduction is permitted which does not comply with these terms *Correspondence: Johanne Tromp, dHJvbXBqb2hhbm5lQGdtYWlsLmNvbQ==; Michael Gaebler, Z2FlYmxlckBjYnMubXBnLmRl Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher 94% of researchers rate our articles as excellent or goodLearn more about the work of our research integrity team to safeguard the quality of each article we publish INDIANAPOLIS — The Indianapolis City-County Council is not moving forward with appointing Kelly Diamond as the director of the Indianapolis Animal Care Services The council voted 21 to 4 to indefinitely table the proposal at Monday's meeting members mentioned the last meeting which had hours of public comment against Diamond and concerns about specific IACS policies Indianapolis Animal Care Services follows a managed intake system meaning the shelter requires an appointment for those looking to surrender an animal A spokesperson tells WRTV this is to ensure there is the space But Humane Society for Hamilton County President and CEO Rebecca Stevens says the strategy places the burden of responsibility on taxpayers local rescues and neighboring shelters like hers They've created a public safety nightmare," Stevens said “They are solely responsible for creating basically an animal welfare crisis in the state of Indiana because they just didn't want to open the doors again." An IACS spokesperson tells WRTV that wait times depend on the type of animal and the situation injured or aggressive may qualify for same-day intake WATCH | New hours at IACS start Saturday; volunteers are upset with the changes Stevens says the wait times can be over five weeks She says this forces people to either take the animal home release it back into the streets or seek another shelter “I've got people who drop the leash in my lobby and leave They go out front and drop the leash and leave They're being dumped at my facility by the hundreds," she said HSHC confirmed it took in 312 Marion County animals in 2024 HSHC estimates it’s spent roughly $852,000 on these animals That cost includes things like searching for the owner Stevens says most of this is funded by Hamilton County taxpayers MORE | IACS needs urgent help, meet fosters stepping in to be part of the solution Cari Klotzsche says the Johnson County Animal Shelter is impacted as well “We probably get at least five phone calls a day from Marion County residents that want to bring in either their dogs or strays they found to our facility," Klotzsche said The facility only has 48 dog kennels and right now there are over 70 dogs in the shelter Klotzsche says her shelter can’t handle the animals turned away from IACS “Their policies cause undue stress for my staff and my shelter and my capacity We’re filling our facility with their animals instead of animals from within our county," she said You can read the full statement from IACS here: Eos Get the most fascinating science news stories of the week in your inbox every Friday It is a great pleasure to cite Anja Klotzsche as the inaugural winner of the AGU Near-Surface Geophysics Early Career Achievement Award Klotzsche’s contributions are remarkable because they combine theoretical methods development with meticulous and creative applications to a range of geological She brought cross-borehole ground-penetrating radar (GPR) data analysis from ray tracing into full-waveform inversion Her work overcame both theoretical challenges and significant practical hurdles for dealing with real borehole data Full-waveform inversion offers significantly higher resolution facilitating a decimeter-scale resolution of the subsurface that opens the door to a range of problems waiting to be solved The value of the full-waveform inversion was quickly recognized internationally Klotzsche has demonstrated the impact of the method on questions related to flow in porous media Remarkably for an early-career investigator Klotzsche has cosupervised the work of 11 Ph.D Many of her recent papers share student coauthorship On top of her exceptional collaborations and mentoring she has been a steady and active contributor to the near-surface geophysics community within both AGU and the Society of Exploration Geophysicists Her impact is a testament to her remarkable ability to solve both theoretical and practical problems and to collaborate productively with investigators from around the globe for the very kind citation and nomination for the Near-Surface Geophysics Early Career Achievement Award I am truly honored to receive this award and deeply grateful to Sarah I have had the great chance to be inspired by and to work with great scientists and friends who guided me and shaped my working life I would not have received this award without many of them and I am sorry that I can name here only a few I was blessed with a great supervisor and mentor While working with him on my master’s thesis I got introduced into the concepts of hydrogeophysics I was so fascinated by these topics that I never left sight of them and they are now the fundaments of my career and postdoc time at the Forschungszentrum Jülich I always found great colleagues and an inspiring environment to broaden my understanding of different fields I had the chance to visit other labs and universities as a visiting scientist These visits allowed me to extend and strengthen my research and cooperation and to broaden the field of applications for the GPR full-waveform inversion and Craig Warren who tremendously inspired me in my career To everyone I have been fortunate enough to work with and to the entire near-surface community: Thank you (2021), Klotzsche receives 2020 Near-Surface Geophysics Early Career Achievement Award, Eos, 102, https://doi.org/10.1029/2021EO160359 Metrics details The production of crops secure the human food supply but climate change is bringing new challenges Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment There are many datasets on above-ground organs of crops but roots and the surrounding soil are rarely the subject of longer term studies we present what we believe to be the first comprehensive collection of root and soil data obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems Soil sensors provide observations at a high temporal and spatial resolution The ongoing measurements cover five years of maize and wheat trials including drought stress treatments and crop mixtures We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods knowledge about soil heterogeneity is crucial to understanding the distribution in soil water and nutrient content The data presented here include information about crop-relevant subsoil data – such as soil water content and root development – on a high temporal-spatial resolution for multiple crop growing periods this kind of MR facility enables insights into processes within the soil–plant continuum at the plot scale while offering high instrumentation for multifaceted observations at high spatial and temporal resolution Since all measures to avoid altered root growth due to tube installation were taken the root parameters are expected to have at most negligible deviations in this respect The data include agronomically relevant information for breeding water-efficient cultivars and for field management under various conditions the root image data provided here can be used to train and benchmark neural networks since deep learning-based technologies are a fast and continuously developing branch of plant and agronomic data analysis are – to the best of our knowledge – the largest available MR image collection the advantage of this image collection is twofold we provide more than 160,000 MR images in one freely available and categorized data set we simultaneously publish reference data that can be used for validation this will help machine learning scientists to develop models soil and plant scientists will benefit directly from the analyzed data The data set was acquired for the years 2016 The data set will thus be added to each year Data for the years 2012–2015 are partly available including measurements on crop development will be published in a corresponding paper we provide a basic overview of the facilities and the data acquisition (b) Aerial photograph of the Selhausen test site and the MR-facilities Both maps are given in WGS 1984 UTM Zone 32 N [m] For (a) and (b) the location of the MR-facilities is given by the blues rectangles the upper terrace facility (Rut) and the lower terrace facility (Rlt) the location of the access trench is indicated with a grey rectangle (c,d) Photos of the soil profiles of the loamy soil at the Rlt (c) and of stony soil at the Rut (d) Overview of the Minirhizotron (MR)-facilities (a) Schematic setup of the MR-facilities indicating that at each of the plots a different agricultural treatment was applied for the different growing seasons The direction of the crop rows is perpendicular to the direction of the rhizotrubes (red arrow) The measurements are carried out from the access trench (c) Overview of one exemplary plot within the MR-facilities with the horizontal crosshole GPR ZOP measurement set up Transmitter and receiver antennae are labeled Tx and Rx Root image measurement are acquired using camera system attached to an index handle (d) Sensor location for one exemplary plot A water reservoir is installed to provide rainwater for irrigation Zoey) was chosen and the shelter needed to be removed due to the height of the crop This resulted in two rainfed plots (Plot 1 and Plot 2) the influence of the sowing date and the planting density was investigated on Plot 1 for Rut and Rlt Overview of the experimental timeline including cultivars and management actions Due to the different rhizotube lengths of both MR facilities the length over which the ZOPs are collected is 6.70 m and 6.40 m resulting in 115 and 109 traces for Rut and Rlt wide-angle reflection and refraction (WARR) measurements are carried out within the access trench Rx antennae are moved over a distance of 6.0 m with a step size of 0.1 m while the Tx antennae are fixed at the zero location At least four calibration measurements per MR facility and measurement day were performed to capture daily variations of the time-zero (see GPR Data Processing section) when considering low-loss and non-magnetic soils the EM velocity v can be transformed into the relative dielectric permittivity εr of the bulk material with an sEIT system is installed and the metal parts interfere with the GPR waves This includes multiple automated steps for thresholding obstacles and filling holes smaller than 0.2 mm as well as the skeletonization of the roots and the feature derivation from the skeletonized roots For information on SWC calculation see Dielectric Permittivity to Soil Water Content section In addition to the soil sensors (see Soil Sensor Data section) the soil water content was measured using the mobile FDR device that employs the HH2 moisture sensor with the ThetaProbe ML3 (ecoTech Umwelt-Messsysteme GmbH the soil moisture was only measured for the topsoil the soil water was measured at depths of 0 m the soil water was measured ten times in each plot of the Rut and eleven times in each plot of the EF over the crop growing season The sensor was always placed between crop rows one folder for each year contains two CSV files – one for all measurements performed on each facility in the corresponding year The root image data repository contains a CSV file for each root trait measured in the corresponding year and facility Implausible permittivity outliers were manually detected and removed the data from the different soil sensors were manually checked for plausibility and unreliable data were excluded The TDR sensor data were filtered for errors in the TDR wave recordings and data for different dates and sensors were excluded Data availability for the measurement seasons 2016–2021 Due to the measurement interval and the sensitivity of the TDR permittivity time series results we suggest applying a median filter or similar filters to the TDR data set to smooth the data as well as to remove the outliers The Topp’s equation is valid for sandy loam to clay and requires the bulk permittivity of the soil (εr) to derive the soil water content (SWC): which considers the different dielectric components of the soil (air we recommend using the CRIM relationship instead of the Topp’s equation due to the high stone content The data corresponding to this paper will be updated regularly on a yearly basis once the analysis is finalized The updated data can be downloaded from these DOIs: GPR data: https://doi.org/10.34731/renq-an61 Root data: https://doi.org/10.34731/jnhr-ke36 Root images: https://doi.org/10.34731/jgd1-tq27 Soil sensor data: https://doi.org/10.34731/rb0q-a208 Additional Information: https://doi.org/10.34731/ke7b-a021 The data measured within the EF were carried out by the project partner at INRES Custom code was used to process the data. For the GPR Data we used MATLAB version: 9.13. 0 (R2022b) to run the codes. The root image processing and soil sensor data is run with Python 3.10.10. Processing codes for the roots images can be found in the Supporting Material for Bauer et al. at https://doi.org/10.34731/pbn7-8g89 The soil water content data measured with the FDR device was processed using R version 4.0.2 The custom codes can not be made publicly accessable due to copyright issues by contacting the corresponding or senior author Alexandratos, N. & Bruinsma, J. World agriculture towards 2030/2050: the 2012 revision. ESA Working Papers https://doi.org/10.22004/ag.econ.288998 (2012) Lynch, J. P. Roots of the second green revolution. Australian Journal of Botany 55, 493–512, https://doi.org/10.1071/BT06118 (2007) Araus, J. L. & Cairns, J. E. Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19, 52–61, https://doi.org/10.1016/j.tplants.2013.09.008 (2014) and Physiology to Understand Soil Foraging 209–221 (Springer International Publishing Silva, D. D. & Beeson, R. C. A large-volume rhizotron for evaluating root growth under natural-like soil moisture conditions. HortScience horts 46, 1677–1682, https://doi.org/10.21273/HORTSCI.46.12.1677 (2011) Wasson, A. P., Nagel, K. A., Tracy, S. & Watt, M. Beyond digging: Noninvasive root and rhizosphere phenotyping. Trends in Plant Science 25, 119–120, https://doi.org/10.1016/j.tplants.2019.10.011 (2020) Thorup-Kristensen, K., Halberg, N., Nicolaisen, M. H., Olesen, J. E. & Dresbøll, D. B. Exposing deep roots: A rhizobox laboratory. Trends in Plant Science 25, 418–419, https://doi.org/10.1016/j.tplants.2019.12.006 (2020) Rasmussen, C. R., Thorup-Kristensen, K. & Dresbøll, D. B. Uptake of subsoil water below 2 m fails to alleviate drought response in deep-rooted chicory (cichorium intybus l.). Plant and Soil 446, 275–290, https://doi.org/10.1007/s11104-019-04349-7 (2020) Taylor, H., Upchurch, D. & McMichael, B. Applications and limitations of rhizotrons and minirhizotrons for root studies. Plant and Soil 129, 29–35, https://doi.org/10.1007/BF00011688 (1990) Van de Geijn, S., Vos, J., Groenwold, J., Goudriaan, J. & Leffelaar, P. The wageningen rhizolab–a facility to study soil-root-shoot-atmosphere interactions in crops: I. description of main functions. Plant and Soil 161, 275–287, https://doi.org/10.1007/BF00046400 (1994) Johnson, M., Tingey, D., Phillips, D. & Storm, M. Advancing fine root research with minirhizotrons. Environmental and Experimental Botany 45, 263–289, https://doi.org/10.1016/S0098-8472(01)00077-6 (2001) Pritchard, S. G., Strand, A. E., McCormack, M. L., Davis, M. A. & Oren, R. Mycorrhizal and rhizomorph dynamics in a loblolly pine forest during 5 years of free-air-co2-enrichment. Global Change Biology 14, 1252–1264, https://doi.org/10.1111/j.1365-2486.2008.01567.x (2008) Svane, S. F., Jensen, C. S. & Thorup-Kristensen, K. Construction of a large-scale semi-field facility to study genotypic differences in deep root growth and resources acquisition. Plant Methods 15, 1–16, https://doi.org/10.1186/s13007-019-0409-9 (2019) Root Characteristics: Why and What to Measure Möller, B. et al. rhizotrak: a flexible open source fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons. Plant and Soil 444, 519–534, https://doi.org/10.1007/s11104-019-04199-3 (2019) Zeng, G., Birchfield, S. T. & Wells, C. E. Rapid automated detection of roots in minirhizotron images. Machine Vision and Applications 21, 309–317, https://doi.org/10.1007/s00138-008-0179-2 (2010) Atkinson, J. A., Pound, M. P., Bennett, M. J. & Wells, D. M. Uncovering the hidden half of plants using new advances in root phenotyping. Current Opinion in Biotechnology 55, 1–8, https://doi.org/10.1016/j.copbio.2018.06.002 (2019) Minervini, M., Scharr, H. & Tsaftaris, S. A. Image analysis: the new bottleneck in plant phenotyping [applications corner]. IEEE signal processing magazine 32, 126–131, https://doi.org/10.1109/MSP.2015.2405111 (2015) Song, P., Wang, J., Guo, X., Yang, W. & Zhao, C. High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. The Crop Journal 9, 633–645, https://doi.org/10.1016/j.cj.2021.03.015 (2021) Kamilaris, A. & Prenafeta-Boldú, F. X. A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science 156, 312–322, https://doi.org/10.1017/S0021859618000436 (2018) Ubbens, J. R. & Stavness, I. Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Frontiers in plant science 8, https://doi.org/10.3389/fpls.2017.01190 (2017) Wang, Y.-H. & Su, W.-H. Convolutional neural networks in computer vision for grain crop phenotyping: A review. Agronomy 12, https://doi.org/10.3390/agronomy12112659 (2022) Yang, W. et al. Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Molecular Plant 13, 187–214, https://doi.org/10.1016/j.molp.2020.01.008 (2020) Klotzsche, A. et al. Monitoring soil water content using time-lapse horizontal borehole GPR data at the field-plot scale. Vadose Zone Journal 18, https://doi.org/10.2136/vzj2019.05.0044 (2019) Yu, Y. et al. Measuring vertical soil water content profiles by combining horizontal borehole and dispersive surface ground penetrating radar data. Near Surface Geophysics 18, 275–294, https://doi.org/10.1002/nsg.12099 (2020) Cai, G. et al. Construction of minirhizotron facilities for investigating root zone processes. Vadose Zone Journal 15, https://doi.org/10.2136/vzj2016.05.0043 (2016) Morandage, S. et al. Root architecture development in stony soils. Vadose Zone Journal 20, https://doi.org/10.1111/10.1002/vzj2.20133 (2021) Schnepf, A., Leitner, D., Bodner, G. & Javaux, M. Editorial: Benchmarking 3d-models of root growth, architecture and functioning. Frontiers in Plant Science 13, https://doi.org/10.3389/fpls.2022.902587 (2022) Vereecken, H. et al. Modeling soil processes: Review, key challenges, and new perspectives. Vadose Zone Journal 15, https://doi.org/10.2136/vzj2015.09.0131 (2016) Landl, M. et al. Modeling the impact of rhizosphere bulk density and mucilage gradients on root water uptake. Frontiers in Agronomy 3, https://doi.org/10.3389/fagro.2021.622367 (2021) Schnepf, A. et al. Linking rhizosphere processes across scales: Opinion. Plant and Soil https://doi.org/10.1007/s11104-022-05306-7 (2022) Landl, M. et al. Modeling the impact of biopores on root growth and root water uptake. Vadose Zone Journal 18, 1–20, https://doi.org/10.2136/vzj2018.11.0196 (2019) Morandage, S. et al. Parameter sensitivity analysis of a root system architecture model based on virtual field sampling. Plant and Soil 438, 101–126, https://doi.org/10.1111/10.1007/s11104-019-03993-3 (2019) Cai, G., Vanderborght, J., Couvreur, V., Mboh, C. M. & Vereecken, H. Parameterization of root water uptake models considering dynamic root distributions and water uptake compensation. Vadose Zone Journal https://doi.org/10.2136/vzj2016.12.0125 (2017) Cai, G. et al. Root growth, water uptake, and sap flow of winter wheat in response to different soil water conditions. Hydrology and Earth System Sciences 22, 2449–2470, https://doi.org/10.5194/hess-22-2449-2018 (2018) Bauer, J. et al. Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions. Biogeochemistry 108, 119–134, https://doi.org/10.1007/s10533-011-9583-1 (2011) Jadoon, K. Z. et al. Estimation of soil hydraulic parameters in the field by integrated hydrogeophysical inversion of time-lapse ground-penetrating radar data. Vadose Zone Journal 11, https://doi.org/10.2136/vzj2011.0177 (2012) Bogena, H. et al. The TERENO-rur hydrological observatory: A multiscale multi-compartment research platform for the advancement of hydrological science. Vadose Zone Journal 17, https://doi.org/10.2136/vzj2018.03.0055 (2018) Brogi, C. et al. Large-scale soil mapping using multi-configuration EMI and supervised image classification. Geoderma 335, 133–148, https://doi.org/10.1016/j.geoderma.2018.08.001 (2019) Pütz, T. et al. TERENO-SOILCan: a lysimeter-network in germany observing soil processes and plant diversity influenced by climate change. Environmental Earth Sciences 75, https://doi.org/10.1007/s12665-016-6031-5 (2016) Lärm, L. et al. Multi-year belowground data of minirhizotron facilities in selhausen: Additional information. TERENO Database https://doi.org/10.34731/st8e-4082 (2023) Weigand, M., Zimmermann, E., Michels, V., Huisman, J. A. & Kemna, A. Design and operation of a long-term monitoring system for spectral electrical impedance tomography (seit). Geoscientific Instrumentation, Methods and Data Systems 11, 413–433, https://doi.org/10.5194/gi-11-413-2022 (2022) Ground penetrating radar theory and applications (elsevier Measuring soil water content with ground penetrating radar Steelman, C. M. & Endres, A. L. Comparison of petrophysical relationships for soil moisture estimation using gpr ground waves. Zone Journal 10, 270–285, https://doi.org/10.2136/vzj2010.0040 (2011) Topp, G. C., Davis, J. L. & Annan, A. P. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resources Research 16, 574–582, https://doi.org/10.1029/wr016i003p00574 (1980) Bauer, F. M. et al. Development and validation of a deep learning based automated minirhizotron image analysis pipeline. Plant Phenomics 2022, https://doi.org/10.34133/2022/9758532 (2022) Smith, A. G. et al. Rootpainter: deep learning segmentation of biological images with corrective annotation. New Phytologist 236, 774–791, https://doi.org/10.1111/nph.18387 (2022) Seethepalli, A. et al. RhizoVision Explorer: open-source software for root image analysis and measurement standardization. AoB PLANTS 13, https://doi.org/10.1093/aobpla/plab056 (2021) Han, E. et al. Digging roots is easier with AI. Journal of Experimental Botany 72, 4680–4690, https://doi.org/10.1093/jxb/erab174 (2021) Schindler, U., Durner, W., von Unold, G. & Müller, L. Evaporation method for measuring unsaturated hydraulic properties of soils: Extending the measurement range. Soil Science Society of America Journal 74, 1071–1083, https://doi.org/10.2136/sssaj2008.0358 (2010) Müller, H.-W., Dohrmann, R., Klosa, D., Rehder, S. & Eckelmann, W. Comparison of two procedures for particle-size analysis: Köhn pipette and x-ray granulometry. Journal of Plant Nutrition and Soil Science 172, 172–179, https://doi.org/10.1002/jpln.200800065 (2009) Lärm, L. et al. Multi-year belowground data of minirhizotron facilities in selhausen: Gpr data. TERENO Database https://doi.org/10.34731/cg3t-nb88 (2023) Lärm, L. et al. Multi-year belowground data of minirhizotron facilities in selhausen: Root data. TERENO Database https://doi.org/10.34731/7x05-2r96 (2023) Lärm, L. et al. Multi-year belowground data of minirhizotron facilities in selhausen: Root images. TERENO Database https://doi.org/10.34731/5zwe-t974 (2023) Lärm, L. et al. Multi-year belowground data of minirhizotron facilities in selhausen: Soil sensor data. TERENO Database https://doi.org/10.34731/ffsk-sy65 (2023) Nguyen, T. H. et al. Comparison of root water uptake models in simulating co 2 and h 2 o fluxes and growth of wheat. Hydrology and Earth System Sciences 24, 4943–4969, https://doi.org/10.5194/hess-24-4943-2020 (2020) Nguyen, T. H. et al. Expansion and evaluation of two coupled root–shoot models in simulating co2 and h2o fluxes and growth of maize. Vadose Zone Journal 21, https://doi.org/10.1002/vzj2.20181 (2022) Zeng, G., Birchfield, S. T. & Wells, C. E. Automatic discrimination of fine roots in minirhizotron images. New Phytologist 177, 549–557, https://doi.org/10.1111/j.1469-8137.2007.02271.x (2008) Hampel, F. R. The influence curve and its role in robust estimation. Journal of the American Statistical Association 69, 383–393, https://doi.org/10.1080/01621459.1974.10482962 (1974) Robinson, D. A., Jones, S. B., Blonquist, J. M. & Friedman, S. P. A physically derived water content/permittivity calibration model for coarse-textured, layered soils. Soil Science Society of America Journal 69, 1372–1378, https://doi.org/10.2136/sssaj2004.0366 (2005) Nguyen, T. H. et al. Responses of winter wheat and maize to varying soil moisture: From leaf to canopy. Agricultural and Forest Meteorology 314, https://doi.org/10.1111/10.1016/j.agrformet.2021.108803 (2022) Download references This work has been partially funded by the German Research Foundation under Germany’s Excellence Strategy and the German Federal Ministry of Education and Research (BMBF) within the framework of the funding initiative “Plant roots and soil ecosystems significance of the rhizosphere for the bio-economy” (Rhizo4Bio) We would like to thank Moritz Harings and Tim Spieker for their cooperation and maintenance of the minirhizotron facilities We would also like to thank all the student assistants for their tremendous effort in acquiring the data We gratefully acknowledge the support of the SFB/TR32 project “Pattern in Soil–Vegetation–Atmosphere Systems: Monitoring and Data Assimilation” funded by the German Research Foundation (DFG) We would also like to thank the Terrestrial Environmental Observatories (TERENO) for providing support at the test site and for meteorological data We also thank Anke Langen and Lutz Weihermüller from the soil physics laboratory at IBG-3 for analyzing the soil samples and Jürgen Sorg for providing technical support with the data repository Open Access funding enabled and organized by Projekt DEAL These authors contributed equally: Lena Lärm Institute of Crop Science and Resource Conservation (INRES) Leibniz Centre for Agricultural Landscape Research (ZALF) installed and maintained the experimental setup and developed the sensing infrastructure data analysis and provided the infrastructure partially conceived and conducted the experiments and analyzed the data The authors declare no competing interests Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Download citation DOI: https://doi.org/10.1038/s41597-023-02570-9 Anyone you share the following link with will be able to read this content: a shareable link is not currently available for this article Sign up for the Nature Briefing newsletter — what matters in science The dates displayed for an article provide information on when various publication milestones were reached at the journal that has published the article activities on preceding journals at which the article was previously under consideration are not shown (for instance submission All content on this site: Copyright © 2025 Elsevier B.V. Bosch is next to come to the “Saxonian Valley” as the supplier decided to build a chip factory in Dresden Construction is planned to start this spring and will cost around one billion euros when they will produce processors to serve e-mobility Bosch says it is making the largest single investment with the factory in its 130 year history near the Dresden airport and is 10 hectares While the supplier is looking at the electronics of electric mobility, batteries are a more difficult matter. The rumored entry into cell production for Bosch is in the air, since CEO Volkmar Denner recently stated that a decision would be made “in a few weeks” Bosch would look into solid-state technology rather than Li-ion battery cells electrive.net (in German) I agree with the Privacy policy electrive has been following the development of electric mobility with journalistic passion and expertise since 2013 we offer comprehensive coverage of the highest quality — as a central platform for the rapid development of this technology <strong>MORGAN COUNTY</strong>Before the arrival of Bob Hynds in 1980 the Indian Creek High School boys&#8217; basketball team had advanced past the sectionals once in its 12-year history.That dramatically changed when Hynds stepped on campus.Indian Creek&#8217;s greatest stretch in its short history came in the early-1980s when the Braves won four-consecutive sectional championships culminating with their lone semi-state appearance in 1983.Those Braves teams had a flair for the dramatic when winning sectional titles &#8212; they beat Center Grove for the 1980 sectional title by three came from behind to beat Franklin in 1981 and beat Whiteland in 1982 by two.The 1983 season with Fetherolf now coaching after Hynds left for the job at Perry Meridian was another matter.Indian Creek rolled over Greenwood for its fourth consecutive sectional and the Braves were far from done.The Braves led 32-24 at the half and pulled away in the third quarter against the Woodmen but it was the following week that probably turned some heads throughout the state.In the afternoon game against Bloomington North Indian Creek easily handled the Cougars 65-51 with Chris Johnston scoring 30 points and Jeff Brownfield 63-61 over Columbus North.The Braves fell behind early in the championship but caught up and led by as many as 11 during the game Indian Creek&#8217;s Carl Klotzsche made a pair of free throws and the Bulldogs&#8217; Chris Conoley&#8217;s last-second shot rolled off the rim.In the Indianapolis semi-state played at Hinkle Fieldhouse Connersville took control early and led 36-15 at halftime.The entire decade was Indian Creek&#8217;s most successful in school history The Braves owned a winning record every year except in 1989 and twice finished the season with 21 wins (in 1982 and 1986).Despite record Artesians make noise at sectionalThe 1980s were easily the worst decade in Martinsville High School boys&#8217; basketball history.But give the Artesians credit.Despite owning a 32.1 winning percentage in the decade the worst in Martinsville&#8217;s history the Artesians tended to peak at the right time.Martinsville reached the sectional championship game five times an impressive accomplishment considering the team never owned a winning season in the decade.Unfortunately for the Artesians they ran into both Bloomington schools &#8212; and several times the season ended in heart-breaking fashion.Two of those losses came in overtime to Bloomington North.In 1981 the Artesians led 34-24 going into the fourth quarter but went cold from there Martinsville missed a combined 11-of-12 shots in the fourth quarter and overtime.The Cougars went on a run in the final quarter but Martinsville was still up with two seconds left when North&#8217;s Mike Minett hit a 28-footer to force overtime.Down by the final margin with 22 seconds David Virgne got the rebound off a missed free throw by Steve Knieper and found Tom Carpentier whose shot was blocked by Minett.Carpentier got the rebound but missed the shot Martinsville had one more chance with the clock about expired but Carpentier missed a full-court desperation shot.Martinsville lost again to the Cougars in the 1982 championship game but it was the 1983 game that must have been particularly disheartening.The Cougars won 70-67 in a triple overtime thriller.Bloomington North led 40-29 with three minutes left in the third quarter when Martinsville went on a 16-5 run to take a 46-45 lead The two teams traded leads through the rest of regulation and entered the first overtime tied at 55.Both teams scored two points in both of the first two overtimes and with Martinsville ahead 63-61 in the third overtime Bloomington North scored six straight.Mark Morin cut the deficit to two but Bloomington North made a pair of free throws Morin made a 20-footer with three seconds left and the Artesians intentionally called timeout knowing they didn&#8217;t have one remaining with the hopes that the Cougars would miss the free throw.They didn&#8217;t and they escaped to play at the regional.And while current coach Tim Wolf&#8217;s arrival in 1988 didn&#8217;t bring immediate results (he won 12 of 41 games his first two seasons) Wooden Gymnasium at the turn of the decade.