All study and patient-related characteristics were extracted using the data extraction tool developed in excel.

This dataset was extracted via Python code from the home page of BBC News. It exists for educational purposes only. The code for extraction into an Excel spreadsheet can be found here: https://www.kaggle.com/code/thomasirvin01/extract-bbc-news-home-page-headlines/notebook.

Supporting information including the data extraction Excel file, the quality assessment scoring sheet, and the Review Manager software file.

This dataset was extracted via Python code from the home page of Fox News. It exists for educational purposes only. The code for extraction into an Excel spreadsheet can be found here: https://www.kaggle.com/code/thomasirvin01/fox-news-homepage-headline-extraction

Contains the NodeJS code for data extraction, processing, and storage, a dump of the final data in a couchDB 1.6 file, and all excel files including the data used in the paper.See Readme.MD for dataprocessing details.Source code is currently in a private GIT repository, just copied here due to need for anonymization.

This file contains participant response data to Likert scale, open-ended responses and self-reported time taken to complete various tasks related to the extraction exercise. This Excel file also contains: 1) Examples of the Interactive HAWC Visuals that can be created after extracting data into the template. 2) The Initial Post-Extraction Survey Tool ("Survey 1") 3) The Final Post-Pilot Survey Tool ("Survey 2") 4) Survey 2 Results: Willingness to Consider Structured Data During Publication Process (Table 2) 5) Survey 1 Results: Participant Self-Reported Time Spent Performing Various Pilot Tasks (Table 3) 6) Survey 1 Results: Summary of Technical Assistance Provided by Team Members (Table 4) 7) Survey 2 Results: Participant Responses Describing Pilot's Impact on Future Research Activities (Table 5) 8) Survey 1 Results: Initial Survey Likert Scale Results (Table 6) 9) Repeat Extraction: Comparison of the First and Second Data Extraction Experience (Among the Same Participant) 10) Survey 1 Results: Problematic & Easy Fields to Extract. This dataset is associated with the following publication: Wilkins, A., P. Whaley, A. Persad, I. Druwe, J. Lee, M. Taylor, A. Shapiro, N. Blanton, C. Lemeris, and K. Thayer. Assessing author willingness to enter study information into structured data templates as part of the manuscript submission process: A pilot study. Heliyon. Elsevier B.V., Amsterdam, NETHERLANDS, 8(3): 1-9, (2022).

Subgroup analysis of intestinal parasite infection by Region.

Excel spreadsheet contain raw data extracted from manuscripts to calculate the infection rate (IR), stepwise dissemination rate (SDR), cumulative dissemination rate (CDR), stepwise transmission rate (STR) and cumulative transmission rate (CTR) presented in Table 5.

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Antimicrobial resistance (AMR) is a silent pandemic that has claimed millions of lives, and resulted in long-term disabilities, limited treatment options, and high economic costs associated with the healthcare burden. Given the rising prevalence of AMR, which is expected to pose a challenge to current empirical antibiotic treatment strategies, we sought to summarize the available data on knowledge, attitudes, and practices regarding AMR in Ethiopia. Articles were searched in international electronic databases. Microsoft Excel spreadsheet and STATA software version 16 were used for data extraction and analysis, respectively. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 checklist was followed. The methodological quality of the studies included was assessed by the Joana Briggs Institute critical appraisal checklists. The random-effect meta-analysis model was used to estimate Der Simonian-Laird’s pooled effect. Statistical heterogeneity of the meta-analysis was checked through Higgins and Thompson’s I2 statistics and Cochran’s Q test. Publication bias was investigated by funnel plots, and the regression-based test of Egger for small study effects with a P value < 0.05 was considered to indicate potential reporting bias. In addition, sensitivity and subgroup meta-analyses were performed. Fourteen studies with a total of 4476 participants met the inclusion criteria. Overall, the pooled prevalence of good AMR knowledge was 51.53% [(95% confidence interval (CI): 37.85, 65.21), I2 = 99.0%, P

Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

Unlocking Data to Inform Public Health Policy and Practice: WP1 Mapping Review Supplementary Excel S1
The data extracted into Excel Tab "S1 Case studies (extracted)" represents information from 31 case studies as part of the "Unlocking Data to Inform Public Health Policy and Practice" project, Workpackage (WP) 1 Mapping Review. Details about the WP1 mapping review can be found in the "Unlocking Data to Inform Public Health Policy and Practice" project report, which can be found via this DOI link: https://doi.org/10.15131/shef.data.21221606

HPV self-sampling has the potential to improve early detection of cervical cancer among women living with HIV (WLHIV), but its acceptability varies, creating implementation challenges, especially in sub-Saharan Africa. This study aims to assess the acceptability of HPV self-sampling among WLHIV. We searched PubMed, Web of Science, CINAHL, Academic Medical Ultimate, Cochrane databases, and Google Scholar. The review protocol was registered with PROSPERO (CRD42022299781). Inclusion criteria were based on population, intervention, comparison, and outcome. Statistical analysis was done with R Studio version 4.3.2, and data abstraction was performed in Microsoft Excel. The analysis included 14 studies on the acceptability of HPV self-sampling among WLHIV. The overall acceptability rate was 73%. The pooled data showed that 94% felt comfortable with self-sampling, 72% found it easy to use, 10% reported pain, 14% felt embarrassed, and 41% felt confident about the process. The study found that a majority of WLHIV accepted HPV self-sampling, a higher rate than in the general female population. Many participants had concerns about the method’s efficacy. This indicates that while WLHIV generally views self-sampling positively, additional education and support are needed to improve their confidence in its accuracy and reliability.

Subgroup analysis of intestinal parasite infection by parasite species.

Convert your PDF quotes into actionable Excel data. The only truly reliable OCR platform to extract the fields of your choice, with API and SDK available.

This references Dirty Excel Data but also includes it extracted ,transformed and loaded quite nicely. There are several sheets:The first sheet is the Dirty Excel Data

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In order to test hypotheses about groundwater flow under and into estuaries and the Atlantic Ocean, geophysical surveys, geophysical probing, submarine groundwater sampling, and sediment coring were conducted by U.S. Geological Survey (USGS) scientists at Cape Cod National Seashore (CCNS) from 2004 through 2006. Coastal resource managers at CCNS and elsewhere are concerned about nutrients that are entering coastal waters via submarine groundwater discharge, which are contributing to eutrophication and harmful algal blooms. The research carried out as part of the study described here was designed, in part, to help refine assumptions required by earlier versions of models about the nature of submarine groundwater flow and discharge at CCNS. This study was conducted in four phases, with a variety of field techniques and equipment employed in each phase. Phase 1 consisted of continuous resistivity profiling (CRP) surveys of the entire study area conducted in 2004. Phase 2 consisted of CRP ground-truthing via resistivity probe measurements and submarine groundwater sampling from hydraulically-drive piezometers using a barge in the Salt Pond/Nauset Marsh area in 2005. Phase 3 consisted of supplemental detailed CRP surveys in the Salt Pond/Nauset Marsh area in 2006. Finally, Phase 4 consisted of sediment coring and porewater extraction in the Salt Pond/Nauset Marsh area later in 2006 to supplement the 2005 sampling.

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