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"en": "Introduction: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients. Objectives: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures. Methods: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell’s C-statistic, respectively. Results: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42–3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08–2.74), 6-month mortality (HR = 2.29; 2.04–2.56), and a longer length of stay (β-coefficient = 2.00; 1.65–2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell’s C = 0.733), and 6-month mortality (Harrell’s C = 0.719). Conclusion: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic."
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"en": "DATA SOURCE AND TIME SERIES DESCRIPTION: \n\nThis permanent laser scanning (PLS) dataset comprises a four-month-long LiDAR point cloud time-series with near-daily temporal resolution. The dataset includes repeated 3D point cloud observations of 106 individual Scots pine (Pinus sylvestris L.) trees located in central Finland. Data were acquired between 1 April and 30 July 2021 using the Finnish Geospatial Institute (FGI) LiDAR Phenology Station (LiPhe). LiPhe is a PLS system located at the Hyytiälä Forestry Field Station in southern Finland. As described by Lindenbergh et al. (2025), PLS can be defined as a fixed, automated terrestrial laser scanning system designed for the continuous monitoring of surfaces and environments, providing high-precision 4D (3D + time) data for change-detection analysis. As of 2026, the LiPhe system is the only PLS actively collecting data in forest environments. LiPhe is equipped with a RIEGL VZ-2000i terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Horn, Austria). The scanner is mounted on a 35 m tower, positioned approximately 15 m above the forest canopy, and operated with a 60° tilt angle to provide an oblique view of the forest stand. Additional details about the LiPhe system are available at Campos et al. (2021). \n\nTemporal Resolution: The median temporal interval between consecutive scans is 24 hours, with a standard deviation of 8.42 hours, totaling 116 time-points. Near-daily coverage was achieved by selecting scans based on favorable meteorological conditions; only acquisitions collected under relative humidity below 80% and wind speeds below 3 m s⁻¹ were included. \n\nSpatial Resolution: The scanner provides a minimum spatial point spacing of approximately 0.01 m at ranges up to 100 m. \n\nGeoreferencing: The dataset consists of georeferenced individual-tree point clouds with three-dimensional coordinates referenced to EPSG:3067 (ETRS89 / TM35FIN) \n\nPoint Cloud Attributes: Each point in the point clouds includes the following attributes: Return number (integer, 1–15), Number of returns (integer, 1–15), Intensity (amplitude), Reflectance (dB), Return pulse deviation (a measure of pulse shape distortion). \n\nNomenclature: The filename denotes the tree id. The single tree point cloud filenames include date and time (YYMMDD_HHMMSS) and treeID: 'YYMMDD_HHMMSS_TREE_TreeId.laz (210401_150501_TREE_5087.laz)'. \n\nMetadata: \n\nTree information can be obtained using Tree2Attributes pipeline: \nhttps://gitlab.com/fgi_nls/public/liphekit_tree2attributes.git \n\nPine_study_tree_metadata.xlsx contains the IDs and coordinates (E, N, H) of the trees studied in this study. All study trees are Scots pine. The coordinates are in the ETRS89/TM35FIN coordinate system. \n\nTree_height_area_timeseries.xlsx contains the height and crown area timeseries of the 106 study trees. Tree height has been calculated using the Tree2Attributes.height function while crown area was calculated with the Tree2Attributes.area function. Date is in yymmdd format and Time is in hhmmss format. Area contains the crown area in m2. Height_99_5, Height_99_95, Height_100, represent tree height in 99.5th, 99.95th and 100th meter percentiles, respectively. 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The PAI value of each tree represents the mean PAI within a 5-m radius from the study tree. \n\n * VCI: Vertical complexity index calculated from the same 0.5-m resolution DTM. The VCI value of each tree represents the mean VCI within a 5-m radius from the study tree. \n\n * Neighbors_total: The number of neighbors within a 5-m radius from the study tree, calculated from the forest inventory data, which includes neighborhood tree positions and their heights. This inventory data is produced in collaboration with another party and cannot be published for this reason. One of the ouputs from the function Tree2Attributes.neighbors. \n\n * NSR: Neighborhood species richness is the number of different species within a 5-m radius. Only Scots pine, Norway spruce, and silver birch are included in the calculation. The values range between 1 (only one species as neighbor) and 3 (all three species as neighbors). NSR is calculated using the forest inventory data, which includes neighborhood tree positions and their heights. This inventory data is produced in collaboration with another party and cannot be published for this reason. One of the ouputs from the function Tree2Attributes.neighbors. \n\n * CI6: Competition index 6 is calculated using the Tree2Attributes.competition function, which uses the forest inventory data as input. However, this inventory data is produced in collaboration with another party and cannot be published for this reason. \n\n * CI8: Competition index 8 is calculated using the Tree2Attributes.competition function, which uses the forest inventory data as input. However, this inventory data is produced in collaboration with another party and cannot be published for this reason. \n\n * CV: Competition index calculated from point clouds of the individual trees and their neighborhood in a 5-m radius. CV is calculated using the Tree2Attributes.competition3D function. The input study tree and neighborhood point clouds were calculated from the full point cloud 210402_180501_R_georef_resample.laz (additional data). \n\n * Dvert: Competition index calculated from point clouds of the individual trees and their neighborhood in a 5-m radius. Dvert is calculated using the Tree2Attributes.competition3D function. The input study tree and neighborhood point clouds were calculated from the full point cloud 210402_180501_R_georef_resample.laz (additional data). \n\n * Growth_onset: The date of the onset of height growth detected using the Tree2Attributes.growth_detection function. A tree height time series has to first be calculated using the Tree2Attributes.height function for all time points. A ready height time series is available in Tree_height_area_timeseries.xlsx. The onset day is given in dd-mm-yy format. \n\n * Growth_onset_DOY: Same as above but date is given in Day Of Year format. \n\n * Absolute_height_change: Absolute difference in height between the mean value in April (before growth) and the mean height in July. Values are in meters. Calculated using the Tree2Attributes.growth_detection function. \n\n * Relative_height_change: Relative difference in height between the mean value in April (before growth) and the mean height in July. The mean height in April is used as base height for the tree to calculate its relative growth in height. Calculated using the Tree2Attributes.growth_detection function. \n\nAdditional data: A full view point cloud from the LiPhe data collection conducted in April 2021 is also available here (resampled with 5 cm resolution), enabling evaluation of tree spatial locations and neighborhood conditions at the beginning of the time series. For this application, as well as height estimation, the 5 cm resample do not affect the results accuracy. Full unsampled Liphe point clouds are available upon request. Additional complementary data related to this dataset are available through LiPheStream (https://doi.org/10.1038/s41597-024-04143-w) which includes a Digital Terrain Model (DTM) of the study area as well as example full point clouds (PLS and ALS).\n\nCampos, M. B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., & Puttonen, E. (2021). A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy. Frontiers in Plant Science, 11, 606752. https://doi.org/10.3389/fpls.2020.606752 \n\nLindenbergh, R., Anders, K., Campos, M., Czerwonka-Schröder, D., Höfle, B., Kuschnerus, M., ... & Vos, S. (2025). Permanent terrestrial laser scanning for near-continuous environmental observations: Systems, methods, challenges and applications. ISPRS Open Journal of Photogrammetry and Remote Sensing, 100094. https://doi.org/10.1016/j.ophoto.2025.100094 \n\nDATA PROCESSING \n\nThis dataset is a subset of LiPhe data collection. Plot-level LiPhe point clouds were processed to generate near-daily individual-tree point clouds using the LiPheKit processing pipeline. The workflow includes point cloud registration, georeferencing, and individual tree segmentation. Further information on the LiPheKit pipeline is available at Wittke et al. (2024). Attention: the goal of this dataset was to estimate tree height over time and subsequently derive tree height curves to identify the onset of vertical growth in Scots pine during the 2021 growing season in Finland. During data processing, the treetops were preserved and prioritized while the data were cleaned; as a result, some structural elements, such as branches, may have been removed due to aggressive automatic tree segmentation applied to eliminate neighboring trees. Most of those trees can be also found on LiPheStream, which is a LiPhe point cloud time series dataset with different temporal resolutions (weekly level). \n\nLiPheKit is available at: https://gitlab.com/fgi_nls/public/liphekit \n\nWittke, S., Campos, M., Ruoppa, L., Echriti, R., Wang, Y., Gołoś, A., ... & Puttonen, E. (2024). LiPheStream-A 18-month high spatiotemporal resolution point cloud time series of Boreal trees from Finland. Scientific Data, 11(1), 1281. https://doi.org/10.1038/s41597-024-04143-w"
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"fi": "<jats:title>Abstract</jats:title><jats:p>In the present paper, we introduce a high-resolution spatiotemporal point cloud time series, acquired using a LiDAR sensor mounted 30 metres above ground on a flux observation tower monitoring a boreal forest. The dataset comprises a 18-month long (April 2020 - September 2021) time series with an average interval of 3.5 days between observations. The data acquisition, transfer, and storage systems established at Hyytiälä (Finland) are named the LiDAR Phenology station (LiPhe). The dataset consists of 103 time points of LiDAR point clouds covering a total of 458 individual trees, comprising three distinct Boreal species. Additional reference information includes the respective location, the species, and the initial height (at the first time point) of each individual tree. The processing scripts are included to outline the workflow used to generate the individual tree point clouds (LiPheKit). The presented dataset offers a comprehensive insight into inter- and intra-species variations of the individual trees regarding their growth strategies, phenological dynamics, and other functioning processes over two growth seasons.</jats:p>",
"und": "<jats:title>Abstract</jats:title><jats:p>In the present paper, we introduce a high-resolution spatiotemporal point cloud time series, acquired using a LiDAR sensor mounted 30 metres above ground on a flux observation tower monitoring a boreal forest. The dataset comprises a 18-month long (April 2020 - September 2021) time series with an average interval of 3.5 days between observations. The data acquisition, transfer, and storage systems established at Hyytiälä (Finland) are named the LiDAR Phenology station (LiPhe). The dataset consists of 103 time points of LiDAR point clouds covering a total of 458 individual trees, comprising three distinct Boreal species. Additional reference information includes the respective location, the species, and the initial height (at the first time point) of each individual tree. The processing scripts are included to outline the workflow used to generate the individual tree point clouds (LiPheKit). The presented dataset offers a comprehensive insight into inter- and intra-species variations of the individual trees regarding their growth strategies, phenological dynamics, and other functioning processes over two growth seasons.</jats:p>"
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"en": "Contains measurement data of air temperature for the manuscript \"Real-time measurements of micro-climatic temperature and relative humidity in the Finnish cities of Tampere, Helsinki and Rovaniemi\" by Kühn et al. (in preparation) of 23 measurement stations in Tampere.\n\nTechnical Info:\n\nNB: Update on 13.1.2026: data file Tampere\\_2025.dat to include all measurements of year 2025.\n\nThe measurements were conducted at a height of 3 m in the locations listed in Tampere\\_Table1\\_New.txt during 07/2023-12/2025.\n\nEach measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf] Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation.\n\nQuality check\n\nThe temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.\n\nThe Local Climate Zones (LCZs) in Table 1. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).\n\nTable Of Contents: \nThe descriptions of the measurement stations are in Tampere\\_Table1\\_New.txt. \nColumns \n1. Station\\_code \n2. Station\\_id \n3. latitude \n4. longitude \n5. elevation above mean se alevel (m) \n6. LCZ\\_global\\_point (LCZ at the grid point nearest to the measurement station) \n7. LCZ\\_global\\_r200 (Mode of the LCZs within a 200-meter radius around the measurement station) \n \nThe data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Tampere\\_YEAR.dat, with Celsius as Unit and missing value -999. \nColumns: \n1. Station\\_code \n2. Timestamp(YMDHH24) UTC \n3. Mean temperature of the previous hour \n4. Minimum temperature of the previous hour \n5. Maximum temperature of the previous hour \n6. Standard deviation of the temperature measurements during the previous hour \n7. Number of measurements during the previous hour (usually 12) \n \nReferences:\n\nAalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651 Stewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1 \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022. \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. Zenodo. https://doi.org/10.5281/zenodo.6364593."
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