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105 results for “market data”
Data and code from: Spatial selection undermines flood protection in U.S. wetland markets
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Data from: United States cattle market location and annual market sales estimate data
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Data from: Pollination treatment affects fruit set and modifies marketable and storable fruit quality of commercial apples
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Data and Code for: Food distribution, but not market forces, predict behavioral social tolerance in rhesus macaques
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Data for: Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels
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Data from: Time to market for drugs approved in Canada between 2014-2018: an observational study
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objectives: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>This study examines the length of time between when a patent application is filed in Canada for a new drug and when it is available for patients (time to market) and various components of that time. It also looks at whether various factors explain the time between patent application to New Drug Submission and compares Canadian and American times. Drugs approved between January 1, 2014 and December 31, 2018 are examined. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Design: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>Descriptive study</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Data Sources: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>Websites from Health Canada, Food and Drug Administration, Merck Index, United States Patent and Trademark Office, World Health Organization and previously published articles.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Interventions: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>None</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Primary and Secondary Outcomes: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>The primary outcomes are time to market, time from patent application to New Drug Submission (pre-NDS time), review time, time from approval to availability (postapproval time) and factors that may influence the pre-NDS time. The secondary outcome is a comparison of Canadian and American review times and times between patent application and approval. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>There were 113 drugs available for analysis. The median time to market was 11.80 years (inter-quartile range (IQR) 9.40, 14.05). The component median times were pre-NDS 10.00 years (IQR 8.05, 12.80), review time 0.96 years (IQR 0.75, 1.15) and postapproval time 0.15 years (IQR 0.08, 0.28). Less than 8% of the pre-NDS time was explained by the factors that were analyzed in a multiple linear regression equation. There was no statistically significant difference between Canadian and American pre-NDS times. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>On average, once a drug reaches the market companies have a median of 8.2 years before the patent expires and generics can reach the market. Most of the time between the filing of a patent application and when a drug is marketed is determined by decisions that are largely under the control of the company.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Quality of evidence considered by Health Canada in granting full market authorization to new drugs with a conditional approval: a retrospective cohort study
Objectives: This study examines the characteristics of studies that Health Canada uses to grant full marketing authorization for products given a conditional approval between January 1, 1998 and June 30, 2017. Design: Cohort study. Data sources: Journal articles listing drugs that fulfilled their conditions and received full marketing authorization, Notice of Compliance database, Notice of Compliance with conditions web site, Qualifying Notices listing required confirmatory studies, clinicaltrials.gov, PubMed, Embase, companies making products being analyzed, journal articles resulting from confirmatory studies. Interventions: None Primary and secondary outcome measures: Characteristics of studies - study design (randomized controlled trials, observational), primary outcome used (clinical, surrogate), blinding, number of patients in studies, patient median age, number of men and women. Results: Eleven companies confirmed 36 publications for 19 products (21 indications). Twenty-nine out of the 36 studies were randomized controlled trials (RCTs) but only 10 stated if they were blinded. Twenty used surrogate outcomes. The median age of patients was 56 (interquartile range (IQR) 44, 61). The median number of men per study/trial was 184 (IQR 58, 514) versus women - 141 (IQR 46, 263). Conclusions: Postmarket studies required by Health Canada had more rigorous methodology than those required by either the Food and Drug Administration or the European Medicines Agency. There were still deficiencies in these studies. The absence of blinding in the majority of RCTs may introduce bias in their results. The use of surrogate outcomes especially in oncology trials means that improvements in survival are not available. The relatively young age of patients, even for products for cancer, means that predicting how the elderly will respond is often unknown. The almost universal finding that men outnumbered number women may make it hard to differentiate responses by sex. These results raise potential concerns about the quality of evidence that Health Canada accepts.
Data from: Association between stock market gains and losses and Google searches
Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people's search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.
Data from: Labor market integration of people with disabilities: results from the Swiss Spinal Cord Injury Cohort Study
Objectives: We aimed to describe labor market participation (LMP) of persons with spinal cord injury (SCI) in Switzerland, to examine potential determinants of LMP, and to compare LMP between SCI and the general population. Methods: We analyzed data from 1458 participants of employable age from the cross-sectional community survey of the Swiss Spinal Cord Injury Cohort Study. Data on LMP of the Swiss general population were obtained from the Swiss Federal Statistics Office. Factors associated with employment status as well as the amount of work performed in terms of full-time equivalent (FTE) were examined with regression techniques. Results: 53.4% of the participants were employed at the time of the study. Adjusted odds of being employed were increased for males (OR=1.73, 95% CI 1.33 - 2.25) and participants with paraplegia (OR=1.78, 95% CI 1.40 - 2.27). The likelihood of being employed showed a significant concave relationship with age, peaking at age 40. The relation of LMP with education was s-shaped, while LMP was linearly related to time since injury. On average, employment rates were 30% lower than in the general population. Males with tetraplegia aged between 40 and 54 showed the greatest difference. From the 771 employed persons, the majority (81.7%) worked part-time with a median of 50% FTE (IRQ: 40%-80%). Men, those with younger age, higher education, incomplete lesions, and non-traumatic etiology showed significantly increased odds of working more hours a week. Significantly more people worked part-time than in the general population with the greatest difference found for males with tetraplegia aged between 40 and 54. Conclusions: LMP of persons with SCI is comparatively high in Switzerland. LMP after SCI is, however, considerably lower than in the general population. Future research needs to show whether the reduced LMP in SCI reflects individual capacity adjustment, contextual constraints or both on higher LMP.
Open Data Set for the article Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 2024, 6 (2), 363-382
<p>The datasets available for open access from the article ‘<em>Ballester, C. and Furió, D. (2024). Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 6 (2), 363-382</em>’ are provided here. This study has been supported by funding from the Spanish Ministry of Science, Innovation, and Universities (Project PGC2018-093645-B-100).</p> <p>The data encompasses the price series of the components of the final wholesale prices, other than the day-ahead market price, at an hourly frequency, from January 2017 to December 2021. In particular, the daily average of the hourly price series of the intraday market, which captures the net effect of the six sessions of the intraday market on the final price, the daily average of the hourly net effect on the final price of the procedure to solve technical constraints, the daily average of the hourly costs resulting from ancillary services and deviation management, the daily average of the hourly costs related to capacity payments and the daily average of the hourly costs associated with the interruptibility service. In addition, we compute the daily average of the hourly series of bids (price and amount) individually submitted by market participants to buy or sell energy, distinguishing between matched and non-matched bids, both in the day-ahead market and in the first session of the intraday market. Other energy-related price series included in the analysis are: the Dutch TTF futures price, the API2 index for the coal price, the EUA futures price, the percentage of hours with 100% use from the France-Spain interconnection, the spread from the France-Spain interconnection, the percentage of water reserves in the reservoirs of the Iberian Peninsula. All shareable data are made available in accordance with open data principles to promote transparency and reproducibility in research. However, there is a specific dataset that we are not authorized to share publicly. Specifically, this includes the data corresponding to the Dutch TTF futures price and the API2 index. Consequently, this dataset is published under restricted access.</p>
Open Data Set for the article Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763
<p><span>The datasets available for open access from the article ‘Furió, D. and Moreno-del-Castillo, J.<em> Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763</em>’ are provided here.</span></p> <p><span>The datasets comprise time series data on wholesale market global prices and quantity demanded by reference suppliers and competing retailers, along with day-ahead market prices for each of the 24 hours of the day spanning from January 1, 2007, to March 31, 2022. These series have been downloaded from the CNMC website. Additionally, primary temperature data were obtained from the Spanish Meteorological Agency’s website. This dataset provided hourly weather information from a nationwide network of weather stations. Given the scope of the demand series data, which reflects the entire Spanish market, the temperature series were constructed to ensure national representativeness. These data were then aggregated to develop comprehensive national temperature series, aligning them with the demand series. <span>Finally, a dummy variable for each day t of the studied period was constructed to capture the business/non-business day effect on electricity demand. It takes the value of 1 if </span></span><span>𝑡</span><span> corresponds to a working day (non-holiday, Monday to Friday) and 0 otherwise. The national holidays considered are as follows: January 1<sup>st</sup>, January 6<sup>th</sup>, May 1<sup>st</sup>, August 15<sup>th</sup>, October 12<sup>th</sup>, November 1<sup>st</sup>, December 6<sup>th</sup>, December 8<sup>th</sup>, and December 25th. Additionally, the corresponding Good Friday for each year included in the study period was also considered.</span></p>
Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects
<p>This is the replication package for the paper “Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects”.</p> <p>In project_curation_selection, there is the curation process of the 100 selected projects including the identification of GitHub repositories and classification of evolution scenarios. </p> <p>In distribution_commits_issues_contributors_market_value_before_after_deploy, data collection from GitHub projects includes the distribution of total commits, contributors, and issues before and after deployment of each investigated project. </p> <p>In analysis_commit_messages, there is qualitative analysis of commit message content from all investigated projects. </p> <p>In the analysis_contributors section, the data focuses on analyzing the profiles of each GitHub contributor involved in the investigated projects.</p> <p>In analysis_market_value_by_project, data refers to the market value and volume of each investigated project. </p> <p>In codes, there are scripts used to obtain the analyzed data.</p> <p> </p>
Modelling input data for the case study of the paper "Uncertainty-Based Market-Clearing Models: A Comparative Analysis of the Dutch, French, and German Markets".
<p>This data package includes the modelling input data to replicate the results of the case study included in the paper "Uncertainty-Based Market-Clearing Models: A Comparative<br>Analysis of the Dutch, French, and German Markets". </p> <p>The case study models the Dutch, French and German day-ahead electricity markets, in which the existing capacities of electricity generation and upward- and downward reserve capacities are considered, in addition to 105 wind output realization scenarios for each simulation day. A detailed description of the case study is provided in the readme file.</p> <p>This supplementary data package includes the following files:</p> <p>- Meta Data – Netherlands.xlsx: Dataset containing the meta data for the Dutch case study</p> <p>- Meta Data – France.xlsx: Dataset containing the meta data for the French case study</p> <p>- Meta Data – Germany.xlsx: Dataset containing the meta data for the German case study</p> <p>- Readme.txt: Includes a detailed description of the data packages</p>
Data and Code for "Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic"
<p>Please see the README.md file for a detailed description of each of the code and data files in this dataset.</p>
IEEE-30 energy system data of multi-period market with intertemporal constraints
<p>This is the dataset that is used for the original article: "Locational marginal pricing in multi-period AC OPF environment"</p> <p>The dataset consists of the following files</p> <ul> <li>Case1.zip</li> <li>Case2.zip</li> <li>Case3.zip</li> <li>case_modifications.py</li> <li>data_spec.py</li> <li>OPF_formulation.pdf</li> </ul> <p>Multiperiod AC OPF is given in OPF_formulation.pdf. Modifications of traditional IEEE 30-node case are given in case_modifications.py</p> <p>The case files incorporate input and output multiperiod AC OPF and LMP decomposition data in csv and pickle formats. Data structure of case files is given in data_spec.py.</p> <p>For python users pickle files are given. Nevertheless, python environment is not required. Specification can be read as a text file. All necessary data are repeated in csv format.</p> <p>Step 1 are to define LMPs of limited energy resources or storage resources that are formed by actual marginal resources from all time periods (first LMP definition in fig. 6 in the paper).</p> <p>Step 2 are to define all other LMPs at price-taking nodes (second LMP definition in fig. 6 in the paper).</p> <p>The following interrelation between Lagrange multipliers, LMP components, and price-bonding factors holds true:</p> <pre>assert np.max(np.abs(output_ramp.sensitivities.dot(output_ramp.offer_gen_data.price).tolist() - output_ramp.ramping_gen_data.price)) < 1e-2 if output_pt_step1.components.shape[0]: step1_pf_filter = (~output_pf.is_limited_energy) & (~output_pf.is_storage) assert np.max(np.abs(output_pt_step1.components.node_price - (output_pt_step1.components.f + output_pt_step1.components.tc_sum + output_pt_step1.components.vc_sum))) < 1e-2 assert (output_pt_step1.components.f - output_pt_step1.w_f.dot(output_pf.node_price[step1_pf_filter])).abs().max() < 1e-2 assert (output_pt_step1.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price[step1_pf_filter]) for w in output_pt_step1.w_tc_list), axis=1 ).sum(axis=1)).abs().max() < 1e-2 assert np.max(np.abs(output_pt_step2.components.node_price - (output_pt_step2.components.f + output_pt_step2.components.tc_sum + output_pt_step2.components.vc_sum))) < 1e-2 assert (output_pt_step2.components.f - output_pt_step2.w_f.dot(output_pf.node_price)).abs().max() < 1e-2 assert (output_pt_step2.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price) for w in output_pt_step2.w_tc_list), axis=1 ).sum(axis=1) ).abs().max() < 1e-2</pre> <p> </p>
Data for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China
<p>This dataset is created for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China.</p> <p>The dataset includes the energy demand for buildings of each province in China, the Centralized and Distributed PV-battery system design of each province in China, The EV and ICEV carbon emission comparison of each province in China, the electricity price in China, The Carbon footprint and NPV calculation of current and future building-transportation system in China.</p>
Supplementary data: model results for "Labor market evolution is a key determinant of global agroeconomic and environmental futures"
<p>This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "<strong>Labor market evolution is a key determinant of global agroeconomic and environmental futures</strong>".</p> <p>The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: <a href="https://github.com/realxinzhao/paper-nc2024-LandBasedCDR-GCAM" target="_blank" rel="noopener">realxinzhao/paper-nc2024-LandBasedCDR-GCAM</a>.</p> <p>In addition, the data were used for generating results used in the paper. See more information at <a href="https://github.com/realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems" target="_blank" rel="noopener">realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems</a>.</p>
Data from: Market forces influence helping behaviour in cooperatively breeding paper wasps
Biological market theory is potentially useful for understanding helping behaviour in animal societies. It predicts that competition for trading partners will affect the value of commodities exchanged. It has gained empirical support in cooperative breeders, where subordinates help dominant breeders in exchange for group membership, but so far without considering one crucial aspect: outside options. We find support for the existence of a biological market in paper wasps, Polistes dominula. We first show that females have a choice of cooperative partners. Second, by manipulating entire subpopulations in the field, we increased the supply of outside options for subordinates, freeing up suitable nesting spots and providing additional nesting partners. We predicted that by intensifying competition for help, our manipulation would force dominants to accept a lower price for group membership. As expected, subordinates reduced their foraging effort following our treatments. We conclude that to accurately predict the amount of help provided, social units cannot be viewed in isolation as has traditionally been done: the surrounding market must also be considered.
ChemoFx® PRO - A Post-Market Data Collection Study
ClinicalTrials.gov study NCT00669422. IPD Sharing: Not stated. Countries: 1. Publications: 4.
A Post-Market Domestic (US) and International Data Collection to Assess the Truliant Knee System
ClinicalTrials.gov study NCT05653102. IPD Sharing: Not stated. Countries: 1. Publications: 3.
ScienceDex guides
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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.