This dataset contains the amount of money paid by UK higher education institutions to seven major publishers (Elsevier, Wiley, Springer, Taylor & Francis, Sage, Oxford University Press, and Cambridge University Press) for academic journals from 2010-14. The data was obtained by sending FOI requests to each institution through the website whatdotheyknow.com These are seven of the largest academic publishers but do not represent the total spend of these institutions on academic journals. UPDATE 08/10/2014: Added figures for 13 more institutions. UPDATE 22/10/2014: Added figures for subscriptions to Elsevier journals. Also includes additional figures for other publishers for 16 institutions. UPDATE 24/10/2014: Added figures for subscriptions to Elsevier journals for 13 more institutions. UPDATE 27/10/2014: Added figures for subscriptions to Elsevier journals for 5 more institutions.

Corresponding paper: O. Schmidt, A. Hawkes, A. Gambhir & I. Staffell. The future cost of electrial energy storage based on experience rates. Nat. Energy 2, 17110 (2017).Link to the paper: http://dx.doi.org/10.1038/nenergy.2017.110This dataset compiles cumulative capacity and product price data for electrical energy storage technologies, including the respective regression parameters to construct experience curves. Please see the paper for a full discussion on experience curves for electrical energy storage technologies and associated analyses on future cost, cumulative investment requirements and economic competitiveness of storage.The dataset also presents the underlying data for Figures 1 to 5 and Supplementary Figures 2 and 3 of the paper.

For many decades, the hyperinflation of subscription prices for scholarly journals have concerned scholarly institutions. After years of fruitless efforts to solve this “serials crisis”, open access has been proposed as the latest potential solution. However, also the prices for open access publishing are high and are rising well beyond inflation. What has been missing from the public discussion so far is a quantitative approach to determine the costs of publishing a scholarly article, such that informed decisions can be made as to appropriate price levels. Here we provide a granular, step-by-step calculation of the costs associated with publishing primary research articles, from submission, through peer-review, to publication, indexing and archiving. We find that these costs range from less than US$200 per article in modern, large scale publishing platforms using post-publication peer-review, to about US$1,000 per article in prestigious journals with rejection rates exceeding 90%. The publication costs for a representative scholarly article today come to lie at around US$400. We discuss the additional non-publication cost items that make up the difference between publication costs and final price. The dataset refers to calculations about the scenarios described in a publication about that topic.

This dataset comprises a portfolio of six projects each having a PMB consisting of a budget derived from a standardised first-principles, bottom-up estimation technique utilising a homogeneous set of resources, both consumable and non-consumable, which are inter-related in a highly complex, multi-dimensional manner with appropriate correlation between quantity, productivity rates and cost rates. The data set also includes detailed time-phased actual costs and progress over the life of each project as well as the time-phased values of revenue claimed for each project. The collection and attribution of 12,139 actual costs and the measurement of progress over a period of just under 109 consecutive weeks is consistent and standardised across all six projects providing a unique differentiator to other datasets.

The data was collected over a continuous 109 week period, and is valid for research in portfolio, program or project management or control of multiple resource-constrained projects, where projects are managed collectively, generally in a standardised manner to support a strategic business aim.The latest revision of the data includes updated information for all eight projects in the form of individual spreadsheets for each project. The updates include:

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All new spread sheets have the pre-fix – EDM. All original datasets remain unchanged.

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Earned Duration Information has been added to each project. The ability to create an EDM baseline and record actual EDM data or progress is simplified using the spreadsheets. Tasks, start and completion dates are simply copied into the spread sheet and the data for the EDM baseline, TPD and TED s-curves is created. The spread sheets also include various mechanisms for recording or estimating work progress.

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Actual start and finish dates have been added to each task for each project. The previous dataset only recorded the progress of each task at the completion of a reporting month. This update provides greater granularity for start and finish dates for each task.

Data used to analyze the Quality and Cost of Diabetes Care in Community Health Centers in the United States

This dataset presents energetic and wearable physiological sensor data from ten healthy subjects performing six physical activities.The activities tested were: walking, incline walking, backwards walking, and running on a treadmill, cycling on a stationary bike, and stair climbing on a stairmill -- all at a variety of speeds and/or intensities (21 total conditions). The following physiological signals were collected from wearable sensors while subjects performed all the activities: - Oxygen consumption and carbon dioxide production- Respiratory exchange ratio- Breath frequency- Minute ventilation - Oxygen saturation (SpO2)- Heart rate- Electrodermal activity- Skin temperature - Accelerations, angular velocity, and magnetic field measured from left/right wrist, left/right ankle, left/right foot, pelvis, and chest (IMUs)- Surface EMG from left/right gluteus maximus, rectus femoris, vastus lateralis, semitendinosis, biceps femoris, medial gastrocnemius, soleus, tibialis anteriorThe data are contained in ten (10) Matlab .mat files (one for each subject). For a complete description of the file structure please see the file: CompleteDataDescription_Ingraham_Ferris_Remy_2018For a complete description of experimental methods please see the published article: Ingraham, Kimberly A., Daniel P. Ferris, and C. David Remy. "Evaluating Physiological Signal Salience for Estimating Metabolic Energy Cost from Wearable Sensors." Journal of Applied Physiology (2019). DOI: 10.1152/japplphysiol.00714.2018Edit history: Version 4 is the most current version (as of 3/12/2019). The only changes made between versions were updates to the CompleteDataDescription.pdf file for completeness. Please direct any correspondence to: Kimberly Ingraham (kaingr@umich.edu)

Cost and effort study data

This dataset contains database pricing received through public records requests to members of the Association of Research Libraries and a few additional non-ARL research libraries. Pricing from the years 2018 to 2024, depending on the institution, is included for three premium academic databases: Scopus from Elsevier, SciFinder from the American Chemical Society’s Chemical Abstract Service, and Web of Science from Clarivate.Change log: zip file name changed to "Public Records Documents"; readme updated to match on 2024-08-14.In PremiumPricingProject_DataArticle_Clean_6-5CBV2.xlsx the University of Indiana was corrected to Indiana University and University of Florida data was added for years 22-23, 23-24, 24-25; in the zip folder added documents for Indiana University; readme updated to match on 2025-02-28.

This dataset shows how nonlinear interference changes by the shaping block length and symbol rate when sphere-shaped lightwaves propagate through fiber.

Generation rates by rate class for Eversource in Connecticut

This dataset contains key characteristics about the data described in the Data Descriptor INVACOST, a public database of the economic costs of biological invasions worldwide. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

This is a little overview of the analyses performed for the manuscript submitted entitled "Negative global-scale association between genetic diversity and speciation rates in mammals".OtherDataThis folder includes data from previously published articles and used for this project. It includes the available geographic coordinates for cyt b sequences, data from Upham et al. 2019 (DR tip speciation rates and used nuclear loci), the trait data and other nuclear datasets. Other Upham et a. 2019 files that were required to get ClaDS speciation rates are in 5.speciation_rate/inputs folder.Figures_tableThis folder includes all the table, and main figures and supplementary figures used in the manuscript, both in pdf and png format. The folder Phylopics includes the phylopic svg files used in figure 1, and it includes link for each in PhylopicLinks.txt. Scripts used in the analyses organized by folders1. Folder_per_family.R:* Extract GenBank sequence for a given family to get metadata per family using NCBI taxonomy database and prepare folders to parallelize SuperCrunch per family 2.nameMatching Scripts that improve how many sequences in GenBank can be matched into Upham et al. 2019 tree3.superCrunch_family Scripts to run alignment of GenBank sequences per species4.genetic_diversityScripts to get genetic diversity per species and the data used for analyses.5.speciation_Rate Scripts to get speciation rates per species and the data used for analyses.6.mutationRate Scripts to get mutation rates per species and the data used for analyses.7.pgls Scripts to run PGLS analyses and the results.8.bmlmScripts to run BMLM analyses and the resultsFigures_v3.RmdRmarkdown file with code used to make all the figures, including some extra models mentioned in the manuscript. It also includes the code used to export the Source Data files used for each figure, versions of the used packages and exports the R working environment.Output as HTML file Figures_v3.html that can be opened in a browser.data.tar.xzSource data file with all the data used to make the figures.

The validation of scientific results requires reproducible methods and data. Often, however, data sets supporting research articles are not openly accessible and interlinked. This analysis tests whether open sharing and linking of supporting data through the PANGAEA® data library measurably increases the citation rate of articles published between 1993 and 2010 in the journal Paleoceanography as reported in the Thomson Reuters Web of Science database. The 12.85% (171) of articles with publicly available supporting data sets received 19.94% (8,056) of the aggregate citations (40,409). Publicly available data were thus significantly (p=0.007, 95% confidence interval) associated with about 35% more citations per article than the average of all articles sampled over the 18-year study period (1,331), and the increase is fairly consistent over time (14 of 18 years). This relationship between openly available, curated data and increased citation rate may incentivize researchers to share their data.

Objective: This study presents data on the time cost and associated charges for common performance validity tests (PVTs). It also applies an approach from cost effectiveness research to comparison of tests that incorporates cost and classification accuracy. Method: A recent test usage survey was used to identify PVTs in common use among adult neuropsychologists. Data on test administration and scoring time were aggregated. Charges per test were calculated. A cost effectiveness approach was applied to compare pairs of tests from three studies using data on test administration time and classification accuracy operationalized as improvement in posterior probability beyond base rate. Charges per unit increase in posterior probability over base rate were calculated for base rates of invalidity ranging from 10 to 40%. Results: Ten commonly used PVTs measures showed a wide range in test administration and scoring time from 1 to 3 minutes to over 40 minutes with associated charge estimates from $4 to $284. Cost effectiveness comparisons illustrated the nuance in test selection and benefit of considering cost in relation to outcome rather than prioritizing time (i.e. cost minimization) classification accuracy alone. Conclusions: Findings extend recent research efforts to fill knowledge gaps related to the cost of neuropsychological evaluation. The cost effectiveness approach warrants further study in other samples with different neuropsychological and outcome measures.

Complete output of additional analyses. This file contains the complete output of the additional analyses (attributes are interacted with respondents’ characteristics). Each worksheet contains results from one characteristic. In each worksheet, the interactions terms are shown for easy assessment across each type of health service while the complete output is provided below. (XLSX 218 kb)

InvaCost is the most up-to-date, comprehensive, standardized and robust data compilation and description of economic cost estimates associated with invasive species worldwide1. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making. The ongoing work made by the InvaCost consortium2,3,4 leads to constantly improving the structure and content of the database (see sections below). The list of actual contributors to this data resource now largely exceeds the list of authors listed in this page. All details regarding the previous versions of InvaCost can be found by switching from one version to another using the “version” button above. IMPORTANT UPDATES: 1. All information, files, outcomes, updates and resources related to the InvaCost project are now available on a new website: http://invacost.fr/2. The names of the following columns have been changed between the previous and the current version: ‘Raw_cost_estimate_local_currency’ is now named ‘Raw_cost_estimate_original_currency’; ‘Min_Raw_cost_estimate_local_currency’ is now named ‘Min_Raw_cost_estimate_original_currency’; ‘Max_Raw_cost_estimate_local_currency’ is now named ‘Max_Raw_cost_estimate_original_currency’; ‘Cost_estimate_per_year_local_currency’ is now named ‘Cost_estimate_per_year_original_currency’3. The Frequently Asked Questions (FAQ) about the database and how to (1) understand it, (2) analyse it and (3) add new data are available at: https://farewe.github.io/invacost_FAQ/. There are over 60 questions (and responses), so there’s probably yours.4. Accordingly with the continuous development and updates of the database, a ‘living figure’ is now available online to display the evolving relative contributions of different taxonomic groups and regions to the overall cost estimates as the database is updated: https://borisleroy.com/invacost/invacost_livingfigure.html5. We have now added a new column called ‘InvaCost_ID’, which is now used to identify each cost entry in the current and future public versions of the database. As this new column only affects the identification of the cost entries and not their categorisation, this is not considered as a change of the structure of the whole database. Therefore, the first level of the version numbering remains ‘4’ (see VERSION NUMBERING section).

CONTENT: This page contains four files: (1) 'InvaCost_database_v4.1' which contains 13,553 cost entries depicted by 66 descriptive columns; (2) ‘Descriptors 4.1’ provides full definition and details about the descriptive columns used in the database; (3) ‘Update_Invacost_4.1’ has details about the all the changes made between previous and current versions of InvaCost; (4) ‘InvaCost_template_4.1’ (downloadable file) provides an easier way of entering data in the spreadsheet, standardizing all the terms used on it as much as possible to avoid mistakes and saving time at post-refining stages (this file should be used by any external contributor to propose new cost data).

METHODOLOGY: All the methodological details and tools used to build and populate this database are available in Diagne et al. 20201 and Angulo et al. 20215. Note that several papers used different approaches to investigate and analyse the database, and they are all available on our website http://invacost.fr/.

VERSION NUMBERING: InvaCost is regularly updated with contributions from both authors and future users in order to improve it both quantitatively (by new cost information) and qualitatively (if errors are identified). Any reader or user can propose to update InvaCost by filling the ‘InvaCost_updates_template’ file with new entries or corrections, and sending it to our email address (updates@invacost.fr). Each updated public version of InvaCost is stored in this figShare repository, with a unique version number. For this purpose, we consider the original version of InvaCost publicly released in September 2020 as ‘InvaCost_1.0’. The further updated versions are named using the subsequent numbering (e.g., ‘InvaCost_2.0’, InvaCost_2.1’) and all information on changes made are provided in a dedicated file called ‘Updates-InvaCost’ (named using the same numbering, e.g., ‘Updates-InvaCost_2.0’, ‘Updates-InvaCost_2.1’). We consider changing the first level of this numbering (e.g. ‘InvaCost_3.x’ ‘InvaCost_4.x’) only when the structure of the database changes. Every user wanting to have the most up-to-date version of the database should refer to the latest released version.

RECOMMENDATIONS: Every user should read the ‘Usage notes’ section of Diagne et al. 20201 before considering the database for analysis purposes or specific interpretation. InvaCost compiles cost data published in the literature, but does not aim to provide a ready-to-use dataset for specific analyses. While the cost data are described in a homogenized way in InvaCost, the intrinsic disparity, complexity, and heterogeneity of the cost data require specific data processing depending on the user objectives (see our FAQ). However, we provide necessary information and caveats about recorded costs, and we have now an open-source software designed to query and analyse this database6.

CAUTION: InvaCost is currently being analysed by a network of international collaborators in the frame of the InvaCost project2,3,4 (see https://invacost.fr/en/outcomes/). Interested users may contact the InvaCost team if they wish to learn more about or contribute to these current efforts. Users are in no way prevented from performing their own independent analyses and collaboration with this network is not required. Nonetheless, users and contributors are encouraged to contact the InvaCost team before using the database, as the information contained may not be directly implementable for specific analyses.

RELATED LINKS AND PUBLICATIONS:

1 Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z

2 Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37. https://doi.org/10.3897/neobiota.63.55260

3 Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions

4 InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/

5 Angulo E, Diagne C, Ballesteros-Mejia L. et al. (2021) Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Science of the Total Environment 775:144441. https://doi.org/10.1016/j.scitotenv.2020.144441

6Leroy B, Kramer A M, Vaissière A-C, Courchamp F and Diagne C (2020) Analysing global economic costs of invasive alien species with the invacost R package. BioRXiv. doi: https://doi.org/10.1101/2020.12.10.419432

This repository includes all code and data for causal inference over the knowledge graph, it includes experiments over four datasets: the synthetic review dataset, the open review dataset, the subset of DBpedia related to the writer, the MIMIC-III (we don't offer the data due to confidential issue).

The data files consist of the DJIA30 financial data (daily prices) and Twitter sentiment data (the number of negative, neutral and positive tweets) for the period of June 1, 2013 until September 18, 2014. The data analysis is described in the following paper: G. Ranco, D. Aleksovski, G. Caldarelli, M. Grčar, I. Mozetič, The effects of Twitter sentiment on stock price returns, PLoS ONE 10(9): e0138441, http://dx.doi.org/10.1371/journal.pone.0138441, 2015.

This work estimated the economic cost incurred by women accessing contraceptive services in Ghana. The overall goal was to estimate both the direct and indirect costs incurred as a result of accessing these services.

This is the simulation code for modelling seawater intrusion in a 2D cross-section island aquifer using SEAWAT.