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opencc-by-4.0May 2024View details →

Black Swift Technologies S0 Data from 24 March 2023 Data Collection Mission

The file contains data from the whole flight on 24 March 2023. The part analyzed in the manuscript is near the end of the flight when the S0 was near 10 m altitude.    File Contents Include:  Format:            netcdf4 Global Attributes:            Conventions     = 'CF-1.8, WMO-CF-1.0'            wmo__cf_profile = 'FM 303- draft'            featureType     = 'trajectory'            platform_name   = ''            flight_id       = 'P3 Drop Test' Dimensions:            obs = 15788 Variables:     lat                   Size:       15788x1            Dimensions: obs            Datatype:   single            Attributes:                        standard_name = 'latitude'                        units         = 'degrees_north'                        axis          = 'Y'     lon                   Size:       15788x1            Dimensions: obs            Datatype:   single            Attributes:                        standard_name = 'longitude'                        units         = 'degrees_east'                        axis          = 'X'     altitude              Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'altitude'                        units         = 'km'                        axis          = 'Z'                        positive      = 'up'                        long_name     = 'altitude_above_sea_level'     time                  Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'time'                        units         = 'seconds since 2023-03-24T22:18:57Z'                        axis          = 'T'                        calendar      = 'standard'     temp                  Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'air_temperature'                        units         = 'K'                        coordinates   = 'lat lon altitude time'     dew_point             Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'dew_point_temperature'                        units         = 'K'                        coordinates   = 'lat lon altitude time'     rel_hum               Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'relative_humidity'                        units         = '1'                        coordinates   = 'lat lon altitude time'     air_press             Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'air_pressure'                        units         = 'Pa'                        coordinates   = 'lat lon altitude time'     wind_speed            Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'wind_speed'                        units         = 'm s-1'                        coordinates   = 'lat lon altitude time'     wind_dir              Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'wind_from'                        units         = 'degree'                        coordinates   = 'lat lon altitude time'     wind_u                Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'x_wind'                        units         = 'm s-1'                        coordinates   = 'lat lon altitude time'     wind_v                Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'y_wind'                        units         = 'm s-1'                        coordinates   = 'lat lon altitude time'     wind_w                Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'downward_air_velocity'                        units         = 'm s-1'                        coordinates   = 'lat lon altitude time'     tsurf                 Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'sea_surface_temperature'                        units         = 'degC'                        coordinates   = 'lat lon altitude time'     laseralt              Size:       15788x1            Dimensions: obs            Datatype:   double            Attributes:                        standard_name = 'laser_height'                        units         = 'm'                        coordinates   = 'lat lon altitude time'                        long_name     = 'laser_height_corrected_with_attitude'

opencc-by-4.0Feb 2024View details →

Collection of chemical occurance data from NORMAN surface water database and other related files

This data set collection includes the downloaded chemical occurrence data from the NORMAN database. It is related with the surface waters. Additionally, this collection also includes the chemical data from the NORMAN database and DSSTox. Curated version of this dataset using the newly developed CleanGeoStreamR R package can be found in:<a href="https://zenodo.org/records/13799955"> Curated NORMAN Data</a>. More details about the applied methods and the development of CleanGeoStreamR can be found in the following scientific paper: <a href="https://doi.org/10.1016/j.ecoinf.2025.103038">Automated Curation of Spatial Metadata in Environmental Monitoring Data</a> (DOI: 10.1016/j.ecoinf.2025.103038).

Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Collection of Forest Resilience Indicators)

This dataset represents the collection of forest indicators for my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor".  It contains a classification of inicators sourced from: <ul> <li> Nikinmaa, L., Lindner, M., Cantarello, E., Jump, A. S., Seidl, R., Winkel, G., & Muys, B. (2020). Reviewing the use of resilience concepts in forest sciences. Current Forestry Reports, 6, 61-80. </li> <li> European Commission. (2023a). Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on a monitoring framework for resilient European forests. Directorate-General for Environment. Lastly retrieved on December 17, 2023 from <a href="https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en">https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en</a>. </li> <li> European Parliament. (2024, February 27). European Parliament legislative resolution of 27 February 2024 on the proposal for a regulation of the European Parliament and of the Council on nature restoration (COM(2022)0304 – C9-0208/2022 – 2022/0195(COD)). Lastly retrieved on April 26, 2024 from <a href="https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html">https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html</a>. </li> </ul>

Sea ice core temperature and salinity data collected during the 2022 SCALE Winter Cruise

Open the record for dataset details and reuse information.

Complete Data Set, Raman spectra for strains A/Nebraska/14/2019 and A/Hawaii/47/2014 collected at 785 nm and 532 nm

This data contains Raman spectra for two different strains of Influenza A;  A/Nebraska/14/2019 which is an H1N1 subtype and A/Hawaii/47/2014 which is an H3N2 subtype. There contains data for 10 separate growth cultures (i.e. 10 files) for each subtype, collected at two different wavelengths; 785 nm and 532 nm. This makes a total of 40 files. Each file has 400 spectra collected. All spectra were collected at 100x with a 5 second accumulation time.

FHIRed MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.

<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> Data has been anonymised prior the publication. The data has been standardized in Fast Healthcare Interoperability Resources (FHIR) data standard.  This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2). This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2). Authors express their gratitude to all the AlmaHealthDB Team. <h2>Instruction MOTU-to-FHIR Importer</h2> <div> <div> The repository includes a Docker Compose setup for importing the MOTU dataset into a HAPI FHIR server, formatted as NDJSON following the HL7 FHIR R4 standards. <h3>Prerequisites</h3> Before you begin, ensure you have the following installed: <ul> <li><a href="https://www.docker.com/get-started/">Docker </a></li> <li><a href="https://docs.docker.com/compose/install/">Docker Compose</a></li> <li><a href="https://www.python.org/downloads/">Python >=3.7</a> </li> <li><a href="https://pypi.org/project/requests/">Requests Python Library</a></li> </ul> </div> <div> <h3>How to run</h3> <ol> <li>First, unzip the <code>dataset</code> directory containing the NDJSON files.</li> <li>Open a terminal or command prompt in the root directory of this repository.</li> <li>Run the command <code>docker-compose up</code> in the terminal to start the Docker containers.</li> <li>Once the containers are up and running, open another terminal window in the root directory of this repository.</li> <li>Run the command <code>python main.py</code> in the terminal to start the data import process.</li> <li>After the import process is complete, you can access the HAPI FHIR server by opening a web browser and navigating to <a href="http://localhost:8082" target="_blank" rel="nofollow noreferrer noopener">http://localhost:8082</a>.</li> </ol>   </div> </div>

opencc-by-4.0Jul 2024View details →

Collection data and molecular datasets for: Defining species-specific seed sourcing strategies for restoration: An example of how to use genetic data to inform seed collections for multiple co-occurring species

Two files for each dataset are provided: Metadata files contain colelcting information for the samples in each molecular dataset as well as the group assignments (species, genetic neighbourhood and sites) used in analyses, saved as an excel spreadsheet. Molecular datasets containing samples and SNPs used in analyses. The data is formatted as a genlight object saved as an RData file that can be read into the R statisical environment and analysed using the 'dartR' package (Gruber et al. 2018).

opencc-by-4.0Jul 2024View details →

Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment

Title: Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment Abstract: As VR technologies continue to evolve and gain popularity, one of their most notable features is connecting with the virtual presence of a person who is not physically present. Understanding the dynamics of user interaction within these environments is crucial, as they can be utilized in various ways, including collaboration, communication, social interactions, or games and entertainment. This paper presents a method for measuring collaboration and co-presence factors of users by developing a real-time data collection and analysis framework. The designed framework focuses on different collaboration and co-presence scenarios and evaluates a comprehensive system for monitoring and analyzing user interactions in VR, employing both physiological sensors and subjective feedback to assess the sense of presence, co-presence, and collaboration quality. Through an extensive literature review, the paper studies how various factors, including avatar realism and communication modalities, influence user engagement and interaction efficacy. The experiment framework’s capability to integrate qualitative and quantitative data provides a deeper understanding of the immersive experience and its impact on collaborative tasks. The results highlight the importance of design choices in VR environments and their implications for human-computer interaction, user performance, and satisfaction. The findings offer practical guidance for developing more effective VR systems for collaborative work and social interaction. Data Description: 1. User Interaction Logs:    - Data Type: Quantitative    - Description: Timestamped logs of user actions and interactions within the VR environment, including movement data, interaction with objects, and communication instances.    - Format: CSV    - Variables: User ID, Timestamp, Action Type, Object Interacted, Coordinates, Duration 2. Physiological Sensor Data:    - Data Type: Quantitative    - Description: Real-time physiological data collected from users during VR sessions, including heart rate, skin conductance, and EEG data.    - Format: CSV,    - Variables: User ID, Timestamp, Heart Rate, Skin Conductance, EEG Channels 3. Avatar Realism and Communication Modalities Data:    - Data Type: Quantitative    - Description: Data evaluating the impact of avatar realism and communication methods (e.g., voice chat, text chat) on user engagement and interaction efficacy.    - Format: CSV, Text    - Variables: User ID, Avatar Type, Communication Modality, Engagement Score, Interaction Quality Feedback 4. Collaboration and Co-presence Metrics:    - Data Type: Quantitative    - Description: Calculated metrics for collaboration efficiency and co-presence, derived from interaction logs and physiological data.    - Format: CSV    - Variables: User ID, Collaboration Efficiency Score, Co-presence Score, Task Performance

opencc-by-4.0Mar 2024View details →

Collective flow of circadian clock information in honeybee colonies (Data)

This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".   Paper: Collective flow of circadian clock information in honeybee colonies   Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.   <h2>Description of included files</h2> All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.   <h3>mean_velocity_2016.csv and mean_velocity_2019.csv</h3> The mean velocity for each bee and age is averaged over 10-minute time windows. Keys: <ul> <li> velocity: Mean euclidean distance of two consecutive points of a bee's hive position. </li> <li> datetime: Date in year-month-day hour:minute:seconds+ms:ns format. </li> <li> age: Age in days. Can be NaN if the bee has no associated death_date. </li> </ul>   <h3>velocity_2088_2019.csv and velocity_5101_2019.csv</h3> The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019. Keys: <ul> <li> velocity: Euclidean distance of two consecutive points of a bee's hive position. </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> datetime: Date in year-month-day hour:minute:seconds+ms:ns format. </li> </ul>   <h3>cosinor_2016.csv and cosinor_2019.csv</h3> A cosinor fit of the velocity per bee for a time window of 3 consecutive days according to the method proposed by <a href="https://doi.org/10.1186/1742-4682-11-16">Cornelissen</a>. Keys: <ul> <li> mesor: Rhythm-adjusted mean of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> amplitude: Amplitude of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> phase: Acrophase of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> p_value: P-value of an F-test for overall significance of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> p_mesor: P-value of the mesor coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> p_amplitude: P-value of the amplitude coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> p_acrophase: P-value of the phase coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> p_reject: P-value of a F-test for model validity. </li> <li> r_squared: R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> r_squared_adj: Adjusted R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points. </li> <li> p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality. </li> <li> p_hom: P-Value of F-test of variance homogeneity of cosinor fit. </li> <li> dw: Durbin-Watson statistic for the independence of the residuals. </li> <li> p_runs: P-value of runs test of independence of residuals. </li> <li> RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model </li> <li> SSPE: Pure error sum of squares of cosinor fit. </li> <li> bee_id: Numeric unique identifier per individual bee. </li> <li> age: Age in days. Can be NaN if the bee has no associated death_date. </li> <li> date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12. </li> <li> n_data_points: Number of data points per cosinor fit. </li> <li> data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data. </li> <li> data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data. </li> <li> data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data. </li> <li> data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data. </li> <li> day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock. </li> <li> day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock. </li> <li> night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock. </li> <li> night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock. </li> <li> ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary. </li> <li> fit_type: Median time bin in seconds used for fit, e.g. 3600 means that the median over a time window of 3600s is used for the fit. </li> <li> ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value. </li> <li> ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value. </li> <li> ci_mesor_lower: Confidence interval lower bound for the mesor fit value. </li> <li> ci_mesor_upper: Confidence interval upper bound for the mesor fit value. </li> <li> ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value. </li> <li> ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value. </li> </ul>   <h3>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</h3> The bee interactions and their post-interaction velocity change. An interaction between two bees (bee0 and bee1) is defined when two bees are detected simultaneously in the hive with a confidence threshold of 0.25, the distance between the markings on their thorax bodies is no more than 14 mm. These interactions are combined into one interaction if the same detections occur within a time interval of 1 second or less between them. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. Keys: <ul> <li> bee_id0: Numeric unique identifier per individual bee. </li> <li> bee_id1: Numeric unique identifier per individual bee. </li> <li> interaction_start: Timestamp indicating interaction start time point. </li> <li> interaction_end: Timestamp indicating interaction end time point. </li> <li> x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start. </li> <li> y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start. </li> <li> theta_start_bee0: Numeric angle of bee relative to hive at interaction start. </li> <li> x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start. </li> <li> y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start. </li> <li> theta_start_bee1: Numeric angle of bee relative to hive at interaction start. </li> <li> x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end. </li> <li> y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end. </li> <li> theta_end_bee0: Numeric angle of bee relative to hive at interaction end. </li> <li> x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end. </li> <li> y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end. </li> <li> theta_end_bee1: Numeric angle of bee relative to hive at interaction end. </li> <li> vel_change_bee0: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction. </li> <li> rel_change_bee0: Numeric post-interaction relative change of velocity: rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction. </li> <li> vel_change_bee1: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction. </li> <li> rel_change_bee1:  Numeric post-interaction relative change of velocity:rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction. </li> </ul> Modeling bees as rectangular mask to determine if bees body overlap when interacting - see more details in <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer repository</a>: <ul> <li> x_trans_focal_bee0: Translated and rotated x-position relative to hive. </li> <li> y_trans_focal_bee0: Translated and rotated y-position relative to hive. </li> <li> theta_trans_focal_bee0: Translated and rotated theta relative to hive. </li> <li> x_trans_focal_bee1: Translated and rotated x-position relative to hive. </li> <li> y_trans_focal_bee1: Translated and rotated y-position relative to hive. </li> <li> theta_trans_focal_bee1: Translated and rotated theta relative to hive. </li> <li> overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap. </li> </ul> Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame: <ul> <li> amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction. </li> <li> phase_bee0: Phase of the cosinor fit per bee of the day of the interaction. </li> <li> p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction. </li> <li> r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction. </li> <li> amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction. </li> <li> phase_bee1: Phase of the cosinor fit per bee of the day of the interaction. </li> <li> p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction. </li> <li> r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction. </li> </ul>   <h3>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</h3> A null model for bee interactions and their post-interaction velocity change. The interaction null model is created by taking the distribution of the start and end times of a given interaction dataframe and selecting two random bees at those times that the bees were detected in the hive at that time. These pairs of bees are considered as "interacting" and their post-interaction speed change is calculated. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. The position data is relative to pixels not to the hive coordinates. As this is a null model the following keys are the same as described in the <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Interaction</a> data frame. Keys: <ul> <li> bee_id0 </li> <li> bee_id1 </li> <li> interaction_start </li> <li> interaction_end </li> <li> x_pos_start_bee0 </li> <li> y_pos_start_bee0 </li> <li> theta_start_bee0 </li> <li> x_pos_start_bee1 </li> <li> y_pos_start_bee1 </li> <li> theta_start_bee1 </li> <li> x_pos_end_bee0 </li> <li> y_pos_end_bee0 </li> <li> theta_end_bee0 </li> <li> x_pos_end_bee1 </li> <li> y_pos_end_bee1 </li> <li> theta_end_bee1 </li> <li> vel_change_bee0 </li> <li> rel_change_bee0 </li> <li> vel_change_bee1 </li> <li> rel_change_bee1 </li> <li> age_bee0 </li> <li> phase_bee0 </li> <li> amplitude_bee0 </li> <li> r_squared_bee0 </li> <li> p_value_bee0 </li> <li> age_bee1 </li> <li> amplitude_bee1 </li> <li> r_squared_bee1 </li> <li> p_value_bee1 </li> <li> phase_bee1 </li> </ul>   <h3>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</h3> Graph-theoretic interaction tree paths. By tracing back interactions that positively influenced the speed of a focal bee, we constructed a graph-theoretic tree structure starting from a young rhythmic bee and recursively adding activating (velocity change parent > 0) individuals. We examined the impact of sequential interactions among bees occurring between 10 am and 3 pm, focusing on a subgroup of n = 1000 bees that are significantly rhythmic, younger than 5 days old, and peak in activity after 12 pm. We limited the time window between interactions to 30 minutes and capped the cascade duration at 2 hours to ensure causal relevance. The resulting interaction trees are collected and each node of all paths in the interaction trees are concatenated to this dataframe. Keys: <ul> <li> bee_id: Numeric unique identifier per individual bee which is a node in the tree. </li> <li> datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place. </li> <li> x_pos: Numeric x-position of bee relative to hive at interaction start. </li> <li> y_pos: Numeric y-position of bee relative to hive at interaction start. </li> <li> vel_change_parent: <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Absolute velocity change</a> of bee of parent node. </li> <li> age: Age in days of node bee. Can be NaN if the bee has no associated death date. </li> <li> is_root: Bool indicating if node is root of tree. </li> <li> depth: Depth of node in tree. E.g. depth of root is 0. </li> <li> is_leaf: Bool indicating if node is leaf of tree. </li> <li> n_children: Number of children of the subtree of the node. </li> <li> parent: Numeric bee_id of parent node. </li> <li> tree_id: Numeric unique identifier of tree. </li> <li> time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction. </li> <li> path_id: Numeric unique identifier of path. </li> </ul> Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame: <ul> <li>  r_squared: R² value of the cosinor fit per bee of the day of the interaction. </li> <li>  phase: Phase of the cosinor fit per bee of the day of the interaction. </li> </ul>   <h2>Software used to acquire and analyze the data:</h2> <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a> <a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a> <a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a> <a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a> <a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a>

opencc-by-4.0Mar 2018View details →

Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year.

Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year out of a significant sample of 1638 tweets.

opencc-by-4.0Apr 2018View details →

Assessing the Integrity of Older Archaeological Collections: An Example from La Ferrassie - supplemental material (data and R code)

Supplemental data and R code for reproducing figures and table values in paper 'Assessing the Integrity of Older Archaeological Collections: An Example from La Ferrassie' by HL Dibble, SC Lin, DM Sandgathe, A Turq.

opencc-by-nc-nd-4.0Nov 2016View details →

FOODSCALE method data collection toolkit

The FOODSCALE method is a food sustainability assessment tool that considers the social, environmental and economic impacts of food production and consumption. It can be applied to any outlet providing food for public consumption such as restaurants, hotels, take-aways, canteens, etc. The FOODSCALE method toolkit includes the survey given to research participants (i.e. restaurants; food outlets), the scoring criteria for the researchers use, and a report sheet that allows the researcher to collate data for each case on a single document (i.e. value; score; data verification method; notes). The FOODSCALE method can be adjusted to suit local contexts.

opencc-by-sa-4.0Jul 2017View details →

APPENDIX A: RAW DATA COLLECTED FROM SELECTED STUDIES

Appendix A: Included studies for Systematic literature review

opencc-by-4.0Sep 2017View details →

TLS Handshake Data Collected By Lumen

This dataset contains TLS handshakes collected from Android devices running the privacy-enhancing app called Lumen between 2015 and 2017. It was used to conduct the first study of TLS usage in Android apps at scale, in a paper titled "Studying TLS Usage in Android Apps" published at ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) 2017. It contains anonymized TLS handshake messages (Client Hello, Server Hello), server certificate chains, and general device information and default TLS settings where available.

opencc-by-4.0Dec 2018View details →

Thaumatin data collected with the SSROX scheme implemented in ZOO system

Using tetragonal thaumatin crystals, we collected SS-ROX datasets with 1° per frame in 30 Hz using a 10 × 18 μm2 beam without any attenuation (6.2 × 1012 photons/frame), absorbed dose was ~8 MGy/frame. Of 23,586 snapshots collected using nine loops, 3,263 snapshots were identified as hits based on the criterion of more than nine spots in the lower resolution range picked by SHIKA. Of these, 2,154 snapshots were indexed and integrated using XDS through kamo.single_images_integration and merged by averaging all observations to 1.4 Å in the Monte-Carlo fashion using kamo.merge_single_integrated_frames. The structure was solved by rigid body refinement using the isomorphous thaumatin structure (PDB code: 1RQW). After a few cycles of manual inspection using Coot and automated refinement with phenix.refine, refinement was converged with Rwork and Rfree values of 0.1906 and 0.2018, respectively. The uploaded file includes datasets collected from nine cryoloops

opencc-by-4.0Feb 2019View details →

Illustrative Darwin core archive to output data from a citizen science platform to a collection management system

Illustrative DwC archive to send data back to a collection management system from a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG. Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42

This upload contains samples 38 - 42 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37

This upload contains samples 33 - 37 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32

This upload contains samples 25 - 32 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 17-24

This upload contains samples 17 - 24 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>