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This dataset includes the Surface Ocean CO2 Atlas (SOCAT) version 3. These data include surface underway, chemical, meteorological, navigational and physical data collected from unknown platforms in the world-wide oceans from 1968-11-16 to 2013-12-31. These data include BAROMETRIC PRESSURE, LATITUDE, LONGITUDE, Partial pressure (or fugacity) of carbon dioxide - water, SALINITY and SEA SURFACE TEMPERATURE. The Surface Ocean CO2 Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biogeochemistry and Ecosystem Research program (IMBER), to deliver a uniformly quality-controlled surface ocean CO2 database.

Introduction The Free-living Food Intake Cycle (FreeFIC) dataset was created by the Multimedia Understanding Group towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the \(3D\) acceleration and orientation velocity signals (\(6\) DoF) from \(22\) in-the-wild sessions provided by \(12\) unique subjects. All sessions were recorded using a commercial smartwatch (\(6\) using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants. Description FreeFIC includes \(22\) in-the-wild sessions that belong to \(12\) unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the \(22\) recordings sums up to \(112.71\) hours, with a mean duration of \(5.12\) hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. # The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Calculate and echo dataset statistics $ python stats_dataset.py # Visualize signals and ground truth $ python viz_dataset.py FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found here and here. Publications If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work: @article{kyritsis2020data, title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE Journal of Biomedical and Health Informatics}, year={2020}, publisher={IEEE}} @inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year={2019}, organization={IEEE}} Technical details We provide the FreeFIC dataset as a pickle. The file can be loaded using Python in the following way: import pickle as pkl import numpy as np with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh) The dataset variable in the snipet above is a dictionary with \(5\) keys. Namely: 'subject_id' 'session_id' 'signals_raw' 'signals_proc' 'meal_gt' The contents under a specific key can be obtained by: sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to \(22\). Elements across all lists are aligned; e.g., the \(3\)rd element of the list under the 'session_id' key corresponds to the \(3\)rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: \([1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]\). It should be emphasized that the subjects with ids \(15, 16, 17, 18, 19\) and \(20\) belong to the held-out part of the FreeFIC dataset (more information can be found in \( \)the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info here and here); i.e., FIC’s subject with id equal to \(2\) is the same person as FreeFIC’s subject with id equal to \(2\). ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between \(1\) and \(5\). It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a \(N_{acc} \times 4\) numpy.ndarray that contains the timestamps in seconds (first column) and the \(3D\) raw accelerometer measurements in \(g\) (second, third and forth columns - representing the \(x, y \) and \(z\) axis, respectively). The data under the 'gyr' key is a \(N_{gyr} \times 4\) numpy.ndarray that contains the timestamps in seconds (first column) and the \(3D\) raw gyroscope measurements in \({degrees}/{second}\)(second, third and forth columns - representing the \(x, y \) and \(z\) axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different \((N_{acc} \neq N_{gyr})\). This behavior is predictable and is caused by the Android platform. proc: list Each element of this list is an \(M\times7\) numpy.ndarray that contains the timestamps, \(3D\) accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the \(x,y\) and \(z\) accelerometer values in \(g\) and the fifth, sixth and seventh columns contain the \(x,y\) and \(z\) gyroscope values in \({degrees}/{second}\). Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of \(100\) Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). meal_gt: list Each element of this list is a \(K\times2\) matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals \(K\) varies across recordings (e.g., a recording exist where a participant consumed two meals). Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - BigO: Big data against childhood obesity. Contact Any inquiries regarding the FreeFIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365 Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

We selected 15 old-growth forest sites spanning six countries across Europe to represent a latitudinal and climatic (temperature and precipitation) gradient (Fig. 1; Table 1). Each site included a study area of approximately 1 ha. The stand ages are unknown. The latitude ranged from 41.47ºN (Elatia, Greece) to 65.25ºN (Litltuva, Norway), with elevations ranging from 172 m (Tjøstøl, Norway) to 1,587 m (Parangalitsa, Bulgaria). Mean annual temperatures varied from 1.1°C (Hilmo and Råndalen, Norway) to 9.2°C (Elatia, Greece), and annual rainfall ranged from 620 mm (Elatia, Greece) to 1,630 mm (Litltuva, Norway). The distance between the two farthest sites (Elatia in Greece and Litltuva in Norway) was approximately 2,755 km. All sites featured a natural predominance of Norway spruce. In total, 644 fallen Norway spruce logs (diameter range: 11–105 cm; average diameter: 32 cm) were investigated in this study (Table 1). We targeted fallen logs >3 m in length and of intermediate decay, which is defined as Hottola and Siitonen’s decay class 3 and 4 (Hottola and Siitonen 2008). The logs in the intermediate decay stage reflect decay types influenced by fungal communities and host the majority of seedlings compared with earlier and later decay stages (Fukasawa 2012; Fukasawa et al. 2018). All surveys were conducted within a 3-m segment of each log where the decay stage was uniform to minimize within-stem decay variations (Pyle and Brown 1999; Kolényová et al. 2024), although the full length of each log was not recorded. The seedling density of Norway spruce per log was calculated by dividing the number of seedlings by the projected area (diameter × length [3 m]) of each log segment, resulting in the number of seedlings per square meter. The seedling was defined as height < 50 cm irrespective of age (Streng et al. 1989). Environmental variables associated with logs were recorded to identify properties affecting spruce seedling colonization. The total basal area (BA) of live Norway spruce trees (>10 cm diameter at breast height) within a 10 × 10 m quadrat around each log was measured. The BA data for sites FR and PG were obtained from the REMOTE Primary Forests project (https://www.remoteforests.org/project.php). The BA data for site ES were derived from a 10 m radius around each log by Condit (1998). At Norwegian sites, the BA data were available for each site but not for each log in the site. Thus, we categorized BA data as site-level data but not as log-level data. Moss cover, bark cover, vegetation on logs, and log–soil contact were measured by eye as a percentage in 10% intervals. Canopy openness was determined for each log via hemispherical photography, with images captured using a Canon EOS Kiss X5 camera equipped with a fisheye lens positioned 30 cm above each log. Color images were converted into black-and-white images and analyzed using CanopOn2 (http://takenaka-akio.org/etc/canopon2/) to measure canopy openness as a percentage of the visible sky through canopy shading. Decay types for sapwood and heartwood were classified separately using the macroscopic criteria of Araya (1993); brown rot is reddish-brown and easily breakable into cubical fragments; white rot is whitish and bleached (yellowish- or grayish-white) and breakable into fibrous fragments; and soft rot, caused mainly by Ascomycota under humid conditions, is dull gray to brown with a mud-like surface. “Fresh” heartwood, still hard with minimal signs of decay, was also recorded. To assess sapwood and heartwood properties, wood samples were collected using an electric drill (Makita, Aichi, Japan) equipped with a 9 × 150 mm wood auger, obtaining samples from three locations at 1-m intervals from both sides of each fallen log. # Latitudinal gradient of Norway spruce log decomposition in Europe and its impact on seedling regeneration [https://doi.org/10.5061/dryad.zs7h44jk9](https://doi.org/10.5061/dryad.zs7h44jk9) ## Description of the data and file structure ### Files and variables #### File: Europe\_for\_analyses\_final.csv **Description:** ##### Variables * Country: country name (categorical) * Site: code name of the site (categorical) * Plot: plot name in the site (categorical) * Log: log ID in the site (categorical) * DC: decay class (order) * Diam_cm: diameter of the log (cm, continuous) * Area: projected area of the log (cm2, continuous) * Bark: bark coverage of the log (%, continuous) * Moss: moss coverage of the log (%, continuous) * Vegetation: vegetation coverage of the log (%, continuous) * Soil: soil contact of the log (%, continuous) * Openness: canopy openness above the log (%, continuous) * Sap_W: presence of white rot in the sapwood of the log (binary) * Sap_B: presence of brown rot in the sapwood of the log (binary) * Sap_S: presence of soft rot in the sapwood of the log (binary) * Heart_W: presence of white rot in the heartwood of the log (binary) * Heart_B: presence of brown rot in the heartwood of the log (binary) * Heart_S: presence of soft rot in the heartwood of the log (binary) * Picea_BA: basal area of Picea abies close to the log (within 10 m radius) (m2/ha, continuous) * Spruce_less10cm: number of spruce seedlings <10 cm height on the log (count) * Spruce_over10cm: number of spruce seedlings ≥10 cm and <50 cm height on the log (count) * Spruce_all: number of all spruce seedlings <50 cm height on the log (count) * Spruce_less10density: density (number divided by projected area of the log) of spruce seedlings <10 cm height on the log (number/m2, continuous) * Spruce_over10density: density (number divided by projected area of the log) of spruce seedlings ≥10 cm and <50 cm height on the log (number/m2, continuous) * Spruce_all_density: density (number divided by projected area of the log) of all spruce seedlings <50 cm height on the log (number/m2, continuous) * Lon: longitude of the study site (˚E, continuous) * Lat: latitude of the study site (˚N, continuous) * Altitude: altitude of the study site (m, continuous) Norway spruce (Picea abies) is a dominant tree species across Eurasia. Understanding its regeneration dynamics is crucial for predicting forest sustainability under climate change. In certain types of Norway spruce forests, regenerates is more successful on decaying logs and stumps than on the forest floor. The type of wood decay, indicative of fungal decay capabilities, markedly influences spruce regeneration, demography, and growth. As fungal distribution and wood decay types are affected by macroclimate, the success of spruce seedling regeneration is expected to vary continentally and may be further influenced by climate change. The present study examined decay type frequencies and seedling densities on decaying Norway spruce logs, assessing effects on regeneration success across 15 sites in 6 European countries along a latitudinal gradient. White rot was dominating in central Europe compared with southern and northern Europe. Spruce seedling densities were positively correlated with white rot in heartwood, particularly influenced by high precipitation seasonality in central Europe. These findings suggest that spruce seedling regeneration on decaying logs may be indirectly affected by climate through its influence on fungal-mediated wood decomposition. Despite predictions of northward shifts in Norway spruce distribution due to climate change, slower regeneration rates on brown rot logs may limit northern expansion, potentially leading to a contraction of the species’ range.

Commonly used series of methods steps regarding fish (e.g. sampling, identification, data management) 2400 geo-referenced distribution data of Greek freshwater fish species.

In the folders, you will find the numerical results obtained from fabricated samples with a fixed porosity of 45 and varying correlation lengths from 15 to 45. More detailed information about the scanning dataset can be found at the following link: https://doi.org/10.18419/darus-3243.

:unav

Additional file 3. Data S3 Detailed information of selected variants from discovery phase.

:unav

A morphosyntactically and semantically annotated corpus of selected sentences from texts throughout the history of the Greek language, produced by the AHRC-funded project “Investigating Variation and Change: Case in Diachrony”