Find research datasets worth reusing

Supporting information for the paper "Evidence of a new population of weak Terrestrial Gamma-ray Flashes observed from aircraft altitude" by I. Bjørge-Engeland et al.

Supporting data for the paper "Evidence of a new population of weak Terrestrial Gamma-ray Flashes observed from aircraft altitude" by I. Bjørge-Engeland et al.

opencc-by-4.0May 2024View details →

Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021

This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format. In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available. The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.

opencc-by-4.0Jun 2024View details →

FlyWire Information Flow Ranks

This repository contains the results of the information flow ranl caluclations used in the analysis around Figure 6 in the manuscript "Neuronal wiring diagram of an adult brain". We applied the information flow model from Schlegel et al. [1] starting from different sets of neurons as indicated by the filename. For each seed set, we ran the model 10,000 times and took the average for each neuron (layer_mean in the feather files).  The feather files can be read using pandas (Python): df = pd.read_feather(<path>) <a href="https://elifesciences.org/articles/66018" target="_blank" rel="noopener">[1] Schlegel et al., Information flow, cell types and stereotypy in a full olfactory connectome. Elife (2021).</a>

A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Bishkek

Full paper is published here <a href="https://doi.org/10.3390/air2040021">https://doi.org/10.3390/air2040021</a> Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan **** Harsh winters, aging infrastructure and control technologies, and the increasing demand for urbanization and modernization of amenities are major factors contributing to the deteriorating air quality in Bishkek, the capital city of Kyrgyzstan and a burgeoning economic hub in Central Asia. The heating energy needs are met via combustion of coal at the central heating plant, heat only boilers, and in-situ heating equipment and the mobility needs via combustion of diesel and petrol. Other mapped sources contributing to daily air pollution levels in Bishkek’s airshed include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with average age more than 10 years, 7.5 km2 of quarries, and one landfill. Annual PM2.5 emission load for the airshed is approximately 5,500 tons, resulting in an annual average concentration of 48 ug/m3, which is 9-10 times higher than the World Health Organization (WHO) guideline of 5 ug/m3. Wintertime daily averages range from 200-300 ug/m3. Proposed emissions management policies for the city include shift to clean fuels like gas and electricity at the heating plants and households, restricting the secondhand vehicle imports and incentivizing newer standard vehicles, promotion of public transport system with newer buses, at least doubling of the waste collection efficiency and landfill management capacity and encouraging greening and maintaining road infrastructure to control dust emissions. PM2.5 levels from mid- to long-term implementation of these options is expected to drop by 50-70%. A long-term plan for Bishkek must include an expansion of the ambient monitoring network using a combination of reference-grade and low-cost sensors to track progress of air quality management efforts and to support information dissemination for public awareness. Files included here: <ol> <li>Composite presentation of supporting information and analysis results File: Bishkek_AQ_Analysis_Composite.pptx This presentation includes summary images of <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Meteorological data summary for the airshed, extracted from the WRF sumulations File: Bishkek_Met_Summary.pptx File: bishkek_meteorology_stats.xlsm (activate macros for stats and making images for final use)</li> <li>GIS files File: gis_bishkek-grids-pop.rar (shapefile and KML file for the airshed grid, csv file for gridded population 20 years, and images) File: gis_bishkek_roads.rar (shapefiles extracted from openstreetmaps) File: gis_bishkek_adm0.rar (shapefiles of administrative boundaries) File: gis_bishkek_multiplelayers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> <li>CAMx output (units is ug/m3) File: bishkek_camx_pm25_monthlyavg.csv (see the grid file for mapping)</li> <li>Gridded model-ready emissions inventory File: bishkek_gridded_emissions_2018.rar (see the grid file for mapping) Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2 Units: tons/grid/year PM2.5 emissions = FPRM + BC + OC PM10 emissions = FRPM + BC + OC + CPRM midlong and midlat are midpoints of grids - see gis_bishkek-grids-pop.rar</li> <li>Ambient monitoring data File: bishkek_monitoring_claritysensor_2021.xlsx File: bishkek_monitoring_us.embassy2019-2024.rar</li> </ol>

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-zeroJul 2024View details →

dryad36/100

Medical publications with information as to whether a publication reports a randomized controlled trial and/or if it covers an oncology topic

Background: Most tools trying to automatically extract information from medical publications are domain agnostic and process publications from any field. However, only retrieving trials from dedicated fields could have advantages for further processing of the data. Dataset collection: A random sample of 900 publications from seven major journals (British Medical Journal, JAMA, JAMA Oncology, Journal of Clinical Oncology, Lancet, Lancet Oncology, New England Journal of Medicine) published between 2010 and 2022 were annotated. Publications that described randomized controlled trials (RCTs) received the label "RCT". Publications that covered oncological topics received the label "ONCOLGY". Trials that fulfilled both criteria were assigned both labels. Trials that were neither RCTs nor covered oncology topics were assigned no label. 100 randomly sampled trials from the New England Journal of Medicine were used as the unseen test set as the journal publishes both oncology and non-oncology articles. Data properties: Each trial is a row in the CSV file. For each trial, there is a doi, a publication date, a title, an abstract, the abstract sections (introduction, methods, results, conclusion), several tags associated with the annotation process (text, _input_hash, _task_hash, options, _view_id, config, accept, answer, _timestamp, _annotator_id,_session_id), and the assigned labels (answer).

opencc-by-4.0Apr 2024View details →

A machine learning framework for extracting information from biological pathway images in the literature

466 target chemicals_selected chemicals: Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.   466 target chemicals_statistics: Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).   arrow detection_bounding box labels: Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".   arrow detection_test dataset: Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).   arrow detection_training and validation datasets: Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.   EBPI outputs: Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.   text classification_training, validation and test datasets: Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.

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>

Missing the (tipping) point: the effect of information about climate tipping points on public risk perceptions in Norway [Dataset]

This is all the data used for the redaction of the research paper "Missing the (tipping) point: the effect of information about climate tipping points on public risk perceptions in Norway".   The dataset is contained in Excel files (.xlsx) and code for statistical analysis can be found in R files (.R)

opencc-by-4.0Mar 2018View details →

Structured Information on State and Evolution of Dockerfiles on GitHub

Docker containers are standardized, self-contained units of applications, packaged with their dependencies and execution environment. The environment is defined in a Dockerfile that specifies the steps to reach a certain system state as infrastructure code, with the aim of enabling reproducible builds of the container. To lay the groundwork for research on infrastructure code, we collected structured information about the state and the evolution of Dockerfiles on GitHub and release it as a PostgreSQL database archive (over 100,000 unique Dockerfiles in over 15,000 GitHub projects). Our dataset enables answering a multitude of interesting research questions related to different kinds of software evolution behavior in the Docker ecosystem.

opencc-by-4.0Jun 2018View details →

Clinical usefulness of library and information services in Japan: The detailed use and value of information in clinical settings

Abstract Objectives Considering that there is a lack of evidence regarding the contribution of library and information services to evidence-based medicine in actual clinical practice in Japan, the purpose of the study is to explore the current status of use and value of library and information services in clinical settings to examine the usefulness of information in implementing evidence-based medicine (EBM) into practice. Methods A Web-based survey was conducted at seven sites (hospitals with 300–1,200 beds) and interviews conducted at five sites to investigate information behavior among health professionals (physicians, residents, and nurses) in 2016, replicating the Value Study carried out in the United States in 2010 and 2011. Using a critical incident technique, respondents answered questions about their information topics, information resources used, search location, access points, and evaluation of the information. Results Analysis from 598 valid responses (275 physicians, 55 residents, and 268 nurses) revealed the characteristics of information use and recognition of the value of information. Physicians and residents showed their information needs regarding clinical care using PubMed (80.4%, 65.5%), Ichushi-Web (61.8%, 63.6%), and UpToDate (40.4%, 65.5%). While physicians rely more on electronic journals (37.8%), residents use more hybrid resources including Japanese print books (38.2%) and online books (30.9% for Japanese, 32.7% for English) to confirm their knowledge. Nurses need more information close to patients and explore a wider variety of information resources such as Japanese print books (60.4%), Ichushi -Web (40.3%), Japanese online books (20.5%), and websites of academic organizations (19.0%). Although the overall recognition of the value of information was relatively modest, concrete changes in clinical practice were found in some areas. Environments with insufficient information and availability of electronic resources should be improved to increase the use of library and information services for implementing EBM.

opencc-by-4.0Apr 2018View details →

Phenotype-Informed Noncoding Element Scoring

Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.

[DATA_SCIENCE] Interviews: The Secure Anonymised Information Linkage Databank (SAIL)

This is a set of interview transcripts executed by Niccolò Tempini between September 2015 and October 2016, as part of the ERC project "The Epistemology of Data-Intensive Science", and in the context of a case study of SAIL. Please read the "Notes on transcript editing" document for further information. Two papers that specifically make use of these interviews have been published as of 2018: <ul> <li>Tempini, N., Leonelli, S., 2018. Concealment and discovery: The role of information security in biomedical data re-use. Soc Stud Sci 48, 663–690. <a href="https://doi.org/10.1177/0306312718804875">https://doi.org/10.1177/0306312718804875</a></li> <li>Tempini, N., 2016. Science Through the “Golden Security Triangle”: Information Security and Data Journeys in Data-intensive Biomedicine, in: Proceedings of the 37th International Conference on Information Systems (ICIS 2016). <a href="https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/">https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/</a></li> </ul> The transcripts document SAIL researchers' experience of infrastructure development and data curation and re-use practices. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter. You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu.

Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.

Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.

Species abundance information improves sequence taxonomy classification accuracy - Qiita data

<a href="https://qiime2.org">QIIME 2</a> Artifacts containing microbiome samples, organised by EMPO 3 classification. Used to test <a href="https://library.qiime2.org/plugins/q2-clawback/">q2-clawback</a>. Downloaded from <a href="https://qiita.ucsd.edu">Qiita</a>. Draws from the following Qiita study ids: 11113[1], 11444, 1716, 10369[2], 990[3], 2080, 1713, 894, 1289, 1883, 1673, 1288, 10353, 2192[4], 10323, 678, 1773, 662, 1799, 864, 1481, 1024[5], 1064, 2182, 10934, 1674, 1795[6], 10273, 10283[7], 10422[8], 804, 10308, 1056[9], 2382[5], 1240, 889, 1041, 1717, 1222, 11149, 11669, 807[10], 10245, 1711, 1721, 910, 1001, 895, 550[11], 1747[12], 713[13], 755, 861, 958[14], 11161[15], 11154[16], 945, 723, 1715, 1714, 10798. References 1. Schulfer, A. F. et al. Nat Microbiol 3, 234–242 (2017). 2. Ruhe, J. et al. Front Plant Sci 7 (2016). 3. O'Brien, S. L. et al. Environ Microbiol 18, 2039–2051 (2016). 4. Lax, S. et al. Science 345, 1048–1052 (2014). 5. Zarraonaindia, I. et al. mBio 6 (2015). 6. Navas-Molina, J. A. et al. in Methods Enzymol 371–444 (2013). 7. Fang, X. et al. Front Microbiol 9 (2018). 8. Tripathi, A. et al. mSystems 3 (2018). 9. Delsuc, F. et al. Mol Ecol 23, 1301–1317 (2013). 10. Gibbons, S. M. et al. PLoS ONE 9, e97435 (2014). 11. Caporaso, J. G. et al. Genome Biol 12, R50 (2011). 12. Hyde, E. R. et al. mSystems 1 (2016). 13. Brazelton, W. J., Nelson, B. & Schrenk, M. O. Front Microbiol 2 (2012). 14. Vitaglione, P. et al. Am J Clin Nutr 101, 251–261 (2014). 15. Spirito, C. M., Marzilli, A. M. & Angenent, L. T. Environ Sci Technol 52, 13438–13447 (2018). 16. Pham, V. T. H. et al. Sci Rep 7 (2017).

Species abundance information improves sequence taxonomy classification accuracy - HMP and NCBI data

Data used to test <a href="https://library.qiime2.org/plugins/q2-clawback/">q2-clawback</a> using taxonomic weights derived from shotgun sequencing experiments. Includes reference sequences and taxonomies derived from the NCBI RefSeq database[1] and paired amplicon and shotgun sequencing results downloaded from the Human Microbiome Project[2]. References 1. O’Leary, N. A. et al. en. Nucleic Acids Res. 44, D733–45 (2016). 2. Huttenhower, C. et al. Nature 486, 207 (2012).

opencc-by-4.0Nov 2018View details →

Fig. 1. – 50 in Enlarging the monotypic Monocarpieae (Annonaceae, Malmeoideae): recognition of a second genus from Vietnam informed by morphology and molecular phylogenetics

Fig. 1. – 50% majority-rule consensus phylogram derived from Bayesian inference of combined seven plastid DNA regions. Bayesian posterior probabilities (PP) indicated on the right; maximum likelihood bootstrap (BS) percentages in the middle; parsimony symmetric resampling (SR) percentages on the left [** denotes BS/SR <50%]. DEN. = Dendrokingstonieae; MAL. = Malmeeae; MIL. = Miliuseae; MON. = Monocarpieae; PIP. = Piptostigmateae. Scale bar unit = substitutions per site.

opencc-by-4.0Feb 2019View details →

Sound source localization with varying amount of visual information in virtual reality [dataset]

This is the dataset that belongs to the publication "Sound source localization with varying amount of visual information in virtual reality". -The file "AVIL_lab.fbx" contains the visual model of the loudspeaker environment that was used throughout the experiment. -The file "localization_task_instruction_final.docx" contains the information sheet that was handed to the subjects before the experiment. -The file "localizationData_public.xlsx" contains the responses from the subjects (column B). The responses in azimuth and elevation (columns F&G) are corrected for the pointing bias (columns H&I).

opencc-by-4.0Feb 2019View details →

Löhr-Limpens, M., Göhringer, F., Schenk, T., & Hesse, C. (2019). 'Grasping and perception are both affected by irrelevant information and secondary tasks: new evidence from the Garner paradigm'. Psychological Research. DOI: 10.1007/s00426-019-01151-z

This repository contains 6 data files and one readme file describing the content of the data files.