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Oxygen isotope constraints on the ventilation of the modern and glacial Pacific: Water fractions and the associated errors
<p>There is two associated datasets linked to the study: Oxygen isotope constraints on the ventilation of the modern and glacial Pacific.</p> <p>One whch contains the values of the 4 end-memebrs defined in the study, and the other one represent the error associated to the reconstruction of each parameter based on the fractions calculated.</p>
An Assessment of Medication Errors among Pediatric Patients in Three Hospitals in Freetown Sierra Leone: Findings and Implications for a Low-Income Country
<p> </p><p><strong>Background</strong></p><p>Pediatric patients are prone to medicine-related problems like medication errors (MEs), which can potentially cause harm. Yet, this has not been studied in this population in Sierra Leone. Therefore, this study investigated the prevalence and nature of MEs, including potential drug-drug interactions (pDDIs) in pediatric patients in three hospitals in Freetown, Sierra Leone.</p><p><strong>Methods: </strong>The study was conducted in three Hospitals among pediatric patients in Freetown and consisted of two phases. Phase one was a cross-sectional retrospective review of prescriptions for completeness and accuracy against standard prescription writing guidelines. Phase two was a point prevalence inpatient chart review of MEs that were categorized into prescription, administration, and dispensing errors and pDDIs. Data was analyzed using frequency, percentages, median, and interquartile range. Kruskal-Wallis H and Mann-Whitney U tests were used to compare the prescription accuracy between the hospitals, with p<0.05 considered statistically significant.</p><p><strong>Results: </strong>In phase one<strong>, </strong>while no prescription attained the global accuracy score (GAS) gold standard of 100%, 106 (29.0%) achieved the 80-100% mark. The patient 63 (17.2%), treatment 228 (62.3%), and prescriber 33 (9.0%) identifiers achieved an overall GAS range of 80-100%. Although the total GAS was not statistically significant (p=0.065), the date (p=0.041), patient (p=<0.001), treatment (p=0.022), and prescriber (p=<0.001) identifiers were statistically significant across the different hospitals. For phase two, the prevalence of MEs was 74 (56.1%), while that for pDDIs was 54 (40.9%). There was a statistically positive correlation between the occurrence of pDDI and the number of medicines prescribed (r=0.211, P=0.015).</p><p><strong>Conclusion: </strong>Low GAS depicts poor compliance with prescription writing guidelines and high prescribing errors. Medication errors were observed at each phase of the medication use cycle, while clinically significant pDDIs were also reported. Thus, there is a need for training on prescription writing guidelines and medication errors.</p><p> </p><p><strong>Keywords:</strong> Pediatrics, Prescription, Medication errors, Drug-drug interactions, Sierra Leone</p>
Appendix: Ferdinando Calori Cesis' transcription errors in the Modena inventory of Pico della Mirandola's library
<p>This Appendix presents the complete list of Ferdinando Calori Cesis’ transcription errors in the Modena inventory of Pico della Mirandola’s library. The Appendix accompanies the article titled "First-language transfer in the copying of Latin manuscripts: the case of Ferdinando Calori Cesis’ transcription of the Modena inventory of Pico della Mirandola’s library", to be published in the Nordic Journal of Renaissance Studies (2024). The columns contain the following information: 1) identifier number, 2) the reading of the inventory followed by Calori Cesis' transcription, separated by a square bracket, 3) whether the error is related to a(n Arabic) number or whether it is an omission, 4) the linguistic classification of the error, 5) whether the error is due to a hypercorrection, 6) whether it involves the transcription of j as i, 7) whether it involves the transcription of u as v, 8) whether it involves the transcription of y as i, and 9) whether the word involves h.</p>
Standard Error Estimates for ARRI Ensemble LM model outputs
<p>Summary</p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p>Description</p> <p>The LM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble linear regression models (LM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble LM model was created using a custom R script that was based off the work detailed in Hogland, 2021. The ensemble LM script was modified to use the lm() function in place of the GAM modelling functions. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble LM models for each cell were averaged (mean) to produce the LM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p>The 246 Restore field plots used to train the LM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p>References:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. <a href="https://doi.org/10.5281/zenodo.8222114">https://doi.org/10.5281/zenodo.8222114</a></p> <p>Credits</p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p>Use Limitations</p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)
<p>This dataset was a part of the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (<a href="https://ijcai-23.org/competitions/">IJCAI'23 Official Website</a>). This dataset repository is divided into 3 versions:</p> <ul> <li><strong><em>Version 1: </em>Training data + Metadata</strong></li> <li><strong><em>Version 2: </em>Test data</strong></li> <li><strong>Version 3: Complete dataset (EEG + EMG) </strong></li> </ul> <p><strong>For more detailed information about the competition, please visit our <a href="http://ijcai-23.dfki-bremen.de/competitions/inter-hri/">competition webpage</a>.</strong></p> <p>This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. </p> <p>The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (<a href="https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/%7D%7D.">https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/).</a> For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). </p> <p><strong>If you use our dataset, cite our paper.</strong></p> <p>Frontiers in Human Neuroscience DOI: <a href="https://doi.org/10.3389/fnhum.2024.1304311">10.3389/fnhum.2024.1304311</a></p> <p>BibTeX citation:</p> <div> <div>@ARTICLE{10.3389/fnhum.2024.1304311,</div> <div>AUTHOR={Kueper, Niklas and Chari, Kartik and Bütefür, Judith and Habenicht, Julia and Rossol, Tobias and Kim, Su Kyoung and Tabie, Marc and Kirchner, Frank and Kirchner, Elsa Andrea},</div> <div>TITLE={EEG and EMG dataset for the detection of errors introduced by an active orthosis device},</div> <div>JOURNAL={Frontiers in Human Neuroscience},</div> <div>VOLUME={18},</div> <div>YEAR={2024},</div> <div>URL={https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311},</div> <div>DOI={10.3389/fnhum.2024.1304311},</div> <div>ISSN={1662-5161}</div> <div>}</div> </div>
Analyzing sources of error in TIRM experiments and data analysis.
<p>The datasets contain the experimental and simulated intensity traces underlying the figures published in the paper "Analyzing sources of error in TIRM experiments and data analysis." by J. A. Rivera-Moran and P. R. Lang in the MDPI Polymers Special issue "Interaction and Dynamics of Polymers and Colloidal Particles Near Interfaces".</p>
A Clinical Study Assessing Critical Errors, Training/Teaching Time, and Preference Attributes of the ELLIPTA® Dry Powder Inhaler, in Comparison to Combinations of Dry Powder Inhalers Used to Provide T
ClinicalTrials.gov study NCT02982187. IPD Sharing: NO. Countries: 2. Publications: 1.
Efficacy of To Be Marketed (TBM) Cholic Acid Capsules Used to Treat Children With Inborn Errors of Bile Acid Synthesis
ClinicalTrials.gov study NCT01115582. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Open Label, Continuation Study of Cholic Acid in Subjects With Inborn Errors of Bile Acid Synthesis
ClinicalTrials.gov study NCT01438411. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Inhaler Device Attributes Investigating Critical and Overall Errors, Ease of Use, and Preference Between a Number of Inhaler Devices
ClinicalTrials.gov study NCT02184624. IPD Sharing: YES. Countries: 2. Publications: 1.
GlycoCholic Acid Treatment for Patients With Inborn Errors in Bile Acid Synthesis
ClinicalTrials.gov study NCT01589523. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Error Enhancement of the Velocity Component
ClinicalTrials.gov study NCT02017093. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Hematopoietic Stem Cell Transplantation (HCT) for Inborn Errors of Metabolism
ClinicalTrials.gov study NCT00668564. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparing Traditional and Biofeedback Telepractice Treatment for Residual Speech Errors
ClinicalTrials.gov study NCT04625062. IPD Sharing: NO. Countries: 1. Publications: 10.
Intervening to Prevent Contextual Errors in Medical Decision Making
ClinicalTrials.gov study NCT00856557. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Correcting Residual Errors With Spectral, Ultrasound, Traditional Speech Therapy
ClinicalTrials.gov study NCT03737318. IPD Sharing: NO. Countries: 1. Publications: 19.
The Intraoperative Technical Errors of Robotic vs. Laparoscopic Radical Right Hemiclectomy
ClinicalTrials.gov study NCT07138859. IPD Sharing: YES. Countries: 1. Publications: 29.
Pictographs for Preventing Wrong-Patient Errors in NICUs
ClinicalTrials.gov study NCT03960099. IPD Sharing: YES. Countries: 1. Publications: 5.
Delineation of Sensorimotor Subtypes Underlying Residual Speech Errors
ClinicalTrials.gov study NCT03736213. IPD Sharing: NO. Countries: 1. Publications: 20.
Safety and Suitability of ICL for Correction of Refractive Errors Without the Use of Dispersive OVDs
ClinicalTrials.gov study NCT06371079. IPD Sharing: Not stated. Countries: 1. Publications: 3.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.