Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,227
datasets available to search
ShareScore release 0.9.0
Dataset results
2,227 results for “markets”
Artificial Intelligence: Professional reference dataset of Artificial Intelligence professional competences analysis based on the job market
<p>Artificial Intelligence vacancies collection to support FAIRsFAIR Artificial Intelligence Professional Competences<br> <br> This dataset is provided as validation and support for the analysis of Artificial Intelligence competences.<br> The dataset includes a collection of vacancies from the job application<br> website <a href="http://indeed.com/">indeed.com</a> that responded to the search term "Artificial Intelligence".</p> <p>The used search term could be easily adjusted in the provided code at <a href="https://github.com/atomcracker/Competence_analysis.git">Github Repository</a>. The heavy extensive research analysis is reflected in graphs, described and reflected in <a href="https://scripties.uba.uva.nl/search?id=727184">Thesis</a>.</p>
European plant-based foods sales data 2017-2020 (Nielsen Market Track)
<ul> <li>The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)</li> </ul> <ul> <li>The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.</li> </ul> <ul> <li>The data were originally collected by Nielsen Market Track. They were analysed on the <a href="http://www.smartproteinproject.eu">Smart Protein project</a> in 2021 and used to publish an extensive <a href="https://smartproteinproject.eu/plant-based-food-sector-report/">market data report</a> and to host a <a href="https://www.youtube.com/watch?v=dsIJqvpXXgw">public webinar</a>, both entitled <em>Plant-based foods in Europe: how big is the market?</em></li> </ul> <p> </p>
CROSSBOW HLU2-UC6-TC1 Economical benefit of mFRR market participation with 5% of energy saved for upward regulation
<p>The energy market participation algorithm of RES-CC allows for participating in DA/ID markets and balancing markets. The process uses the energy generation forecasting of the plants to generate the energy bids for the DA/ID market. With regards to the mFRR market, the whole amount of curtailable energy is offered for downward regulation. Additionally, the process is configurable so that it can save a given amount of the potential generation, do not offering this energy in the DA/ID market but offering it for upward regulation in the mFRR. This upward regulation can only be done with Renewables by means of not selling part of the forecasted energy. This is a risk, as the upward regulation energy might not be requested by the operator, and thus the profit for the generation will be lost.</p> <p>This dataset contains the results of the market participation algorithm running for a week, configured for offering 5% of the forecasted energy for upward regulation.</p> <p>Dataset contains the results for the CROSSBOW portfolios of Croatia, Bulgaria, Romania and Greece.</p> <p>For each country, hour by hour, the following information is provided:</p> <ul> <li>Energy sold in IDM</li> <li>Forecasted generation</li> <li>Energy sold mFRR down</li> <li>Energy sold mFRR up</li> <li>Energy price mFRR down</li> <li>Energy price mFRR up</li> <li>Energy price in IDM</li> <li>Energy looses (saved and not sold in mFRR up)</li> </ul>
CROSSBOW HLU2-UC6-TC1 Economical benefit of mFRR market participation with 20% of energy saved for upward regulation
<p>The energy market participation algorithm of RES-CC allows for participating in DA/ID markets and balancing markets. The process uses the energy generation forecasting of the plants to generate the energy bids for the DA/ID market. With regards to the mFRR market, the whole amount of curtailable energy is offered for downward regulation. Additionally, the process is configurable so that it can save a given amount of the potential generation, do not offering this energy in the DA/ID market but offering it for upward regulation in the mFRR. This upward regulation can only be done with Renewables by means of not selling part of the forecasted energy. This is a risk, as the upward regulation energy might not be requested by the operator, and thus the profit for the generation will be lost.</p> <p>This dataset contains the results of the market participation algorithm running for a week, configured for offering 20% of the forecasted energy for upward regulation.</p> <p>Dataset contains the results for the CROSSBOW portfolios of Croatia, Bulgaria, Romania and Greece.</p> <p>For each country, hour by hour, the following information is provided:</p> <ul> <li>Energy sold in IDM</li> <li>Forecasted generation</li> <li>Energy sold mFRR down</li> <li>Energy sold mFRR up</li> <li>Energy price mFRR down</li> <li>Energy price mFRR up</li> <li>Energy price in IDM</li> <li>Energy looses (saved and not sold in mFRR up)</li> </ul>
CROSSBOW HLU3-UC1-TC1 & HLU3-UC3-TC1 AM Market prices in the demonstration period
<p>CROSSBOW RES-CC continuously participates in the CROSSBOW AM ID market. The dataset comprises some of the market results that are used in the demonstrations. Fields are:</p> <ul> <li>Time slot (hourly)</li> <li>BSP (data is aggregated by hour and BSP)</li> <li>Energy forecasted</li> <li>Energy price in ID market</li> </ul>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. 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> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
When Less is More: Improving Choices in Health Insurance Markets
<p>We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrollment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modeling reveals large ``choice inconsistencies'' such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of ``approximate inertia'' where enrollees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrollees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or ``choice overload''. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization. </p>
Capital markets in 16th-century 's-Hertogenbosch (Bois-le-duc)
<p>This csv-file contains a simple transcription of the so-called Bosch' Protocol, the aldermen's registers of the city of 's-Hertogenbosch, for the period October 1508 to August 1512. I transcribed this source during my master's research.<br> This source documents real estate and annuity transactions in the wide region of 's-Hertogenbosch (the Meierij).<br> </p>
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
<p>This data is used for Stock Market Prediction. </p>
Influence of social networks as a distribution channel in volatile markets of non-fungible tokens..
<p>Dataset with tweets, prices (ETH & USD), volume (USD) and sentiment from BAYC, WOW and CoolCats NFTs. </p> <p>Obtained from Twitter and from the Ethereum blockchain using Dune Analytics (Dune.xyz)</p>
The Huanan Market Origin of SARS-CoV-2 is unlikely: The ancestral lineage containing specimen appears to have arisen from laboratory contamination
<p>• There is universal agreement that the lineage B/L is not the ancestral SARS-CoV-2; lineage A/S is the most ancestral lineage<br> • Until the Gao paper, no lineage A/S virus was identified at the Huanan Market, making the market an unlikely origin for the pandemic<br> • The Gao paper found one specimen, A20, with both lineage A/S and B/L<br> • The lineage B/L reads in A20 and the specimen Ct matched the expected findings<br> • The lineage A/S reads were anomalously high compared to the Ct and meet the definition of a statistical outlier<br> • The SARS-CoV-2 reads from the A20 sample also had two SNVs not seen in GISAID sequences until at least 60-90 days after the specimen was collected from the market<br> • This analysis supports a finding that the ancestral lineage A/S virus sequences in sample A20 were not present on the glove on January 1, 2020 when the sample was collected but instead arose by inadvertent laboratory contamination later, probably during metagenomic sequencing</p> <p>The absence of an unimpeachable ancestral lineage specimen at the Hunan Market makes it unlikely the market was the origin of the pandemic.</p>
Bigger telcos are not necessarily better for infrastructure: A case study of EU versus US markets
<p>This is data and slides for an anticipated forthcoming publication.</p> <p>Telecommunications companies (telcos) provide infrastructure essential to the delivery of digital content. Further, investment in next-generation communication technologies is also seen as critical to overall competitiveness of a market. This dataset results from an examination of the case to be made for European telco consolidation, through comparison with both telcos in the more-concentrated US market, and with other corporations involved in the information or ``eye-ball'' value chain. We find that both profits and growth for EU and US telcos are already comparable before investment in infrastructure, and that in line with standard theory, more value is returned to customers in the form of infrastructure investment in the less-concentrated, EU market. Profits are also in line with other companies in the value chain, with the notable exception of the extremely-concentrated digital ad exchanges segment. </p> <p>The data for the charts was collected from Bloomberg, so we therefore have protected the primary datasheet, available on specific request.</p> <p>No discrepancies with information available from other public sources was identified in respect of the data on revenue. However, companies do not report operating profit (EBIT) and EBITDA systematically in the same manner. We based our calculation on the Bloomberg adjusted EBIT and EBITDA. We thank Benedikt Ströbl for comparing the Bloomberg revenue, EBIT and EBITDA figures with other available sources for all companies in the sample. In particular, the data from Bloomberg was compared to data from Alphaquery and 10-K and annual reports.</p> <p>The below is a non-exhaustive list of the data points for which the Bloomberg adjusted data displayed a delta compared to the data that could be collected from the public sources used for verification, where only some years displayed a delta in the data the year is specified in brackets: (i) in respect of EBIT: Publicis (2018, 2019), NYT (2017) and Axel Springer (2017 and 2019); (ii) in respect of EBITDA (additionally to EBIT list): Verizon, Bertelsmann (2018), Interpublic (2019).</p> <p>Finally, to avoid any confusion in respect of the segment data for Alphabet, the data is presented as retrieved from Bloomberg in full on the tab “Alphabet”, data from the SEC reports used on top of the Bloomberg data to estimate the EBITDA is also reproduced on this tab.</p>
Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market
<p>“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.</p>
Unveiling Mexico City's Airbnb Market Trends: A Data Analysis Exploration.
<p>The present document focuses on analyzing, exploring and visualizing data from the Airbnb market in Mexico City using tools such as Python. The data used for this work was cleaned and prepared to extract relevant insights and relations between popular locations, occupancy rate, and reviews (classified into positive or negative using BERT) of accommodations and market trends. The results obtained provide an in-depth insight into the Airbnb market in Mexico City and may be valuable for property owners to make an informed decision in their dynamic pricing strategy.</p>
Фототаблица 3 Plate 3 A–D – Anisocorbula venustа (Gould, 1861): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 7.5 мм, ЗМ ДВФУ № 38636/Bv-5916; E, F – Diplodonta semiasperoides Nomura, 1932: СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 20.0 мм, ЗМ ДВФУ № 38634/Bv-5914; G, H – Protothaca (Novathaca) jedoensis (Lischke, 1874): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 40.1 мм, ЗМ ДВФУ № 38358/Bv-5774; I, J – Felaniella usta (Gould, 1861): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 33.7 мм, ЗМ ДВФУ № 38354/Bv-5770; K–N – Ruditapes philippinarium (A. Adams et Reeve, 1850): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 18.6 мм, ЗМ ДВФУ № 38618/Bv-5899; O–R – Gomphina (Macridiscus) melanaegis Römer, 1860: СевернаЯ КореЯ, провинциЯ Северный Хамгён, ЁмбудЖин, длина 44.1 мм, ЗМ ДВФУ № 38350/Bv-5766; S–V – Callista (Ezocallista) brevisiphonata (Carpenter, 1864): СевернаЯ КореЯ, провинциЯ Северный Хамгён, г. ЧхондЖин, рынок, длина 76.3 мм, ЗМ ДВФУ № 38356/Bv-5772; W –Z – Protothaca (Protothaca) euglypta (Sowerby III, 1914): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 42.1 мм, ЗМ ДВФУ № 38359/Bv-5775. A–D – Anisocorbula venustа (Gould, 1861): North Korea, North Hamgyong Province, Jipsam, shell length 7.5 mm, ZMFU no. 38636/Bv-5916; E, F – Diplodonta semiasperoides Nomura, 1932: North Korea, North Hamgyong Province, Jipsam, shell length 20.0 mm, ZMFU no. 38634/Bv-5914; G, H – Protothaca (Novathaca) jedoensis (Lischke, 1874): North Korea, North Hamgyong Province, shell length 40.1 mm, ZMFU no. 38358/Bv-5774; I, J – Felaniella usta (Gould, 1861): North Korea, North Hamgyong Province, shell length 33.7 mm, ZMFU no. 38354/Bv-5770; K–N – Ruditapes philippinarium (A. Adams et Reeve, 1850): North Korea, North Hamgyong Province, Jipsam, shell length 18.6 mm, ZMFU no. 38618/Bv-5899; O–R – Gomphina (Macridiscus) melanaegis Römer, 1860: North Korea, North Hamgyong Province, Yombunjin, shell length 44.1 mm, ZMFU no. 38350/Bv-5766; S–V – Callista (Ezocallista) brevisiphonata (Carpenter, 1864): North Korea, North Hamgyong Province, Chongjin City, market, shell length 76.3 mm, ZMFU no. 38356/Bv-5772; W –Z – Protothaca (Protothaca) euglypta (Sowerby III, 1914): North Korea, North Hamgyong Province, shell length 42.1 mm, ZMFU no. 38359/Bv-5775. in On the bivalve molluscan fauna of North Hamgyong Province (North Korea)
Фототаблица 3 Plate 3 A–D – Anisocorbula venustа (Gould, 1861): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 7.5 мм, ЗМ ДВФУ № 38636/Bv-5916; E, F – Diplodonta semiasperoides Nomura, 1932: СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 20.0 мм, ЗМ ДВФУ № 38634/Bv-5914; G, H – Protothaca (Novathaca) jedoensis (Lischke, 1874): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 40.1 мм, ЗМ ДВФУ № 38358/Bv-5774; I, J – Felaniella usta (Gould, 1861): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 33.7 мм, ЗМ ДВФУ № 38354/Bv-5770; K–N – Ruditapes philippinarium (A. Adams et Reeve, 1850): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 18.6 мм, ЗМ ДВФУ № 38618/Bv-5899; O–R – Gomphina (Macridiscus) melanaegis Römer, 1860: СевернаЯ КореЯ, провинциЯ Северный Хамгён, ЁмбудЖин, длина 44.1 мм, ЗМ ДВФУ № 38350/Bv-5766; S–V – Callista (Ezocallista) brevisiphonata (Carpenter, 1864): СевернаЯ КореЯ, провинциЯ Северный Хамгён, г. ЧхондЖин, рынок, длина 76.3 мм, ЗМ ДВФУ № 38356/Bv-5772; W –Z – Protothaca (Protothaca) euglypta (Sowerby III, 1914): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 42.1 мм, ЗМ ДВФУ № 38359/Bv-5775. A–D – Anisocorbula venustа (Gould, 1861): North Korea, North Hamgyong Province, Jipsam, shell length 7.5 mm, ZMFU no. 38636/Bv-5916; E, F – Diplodonta semiasperoides Nomura, 1932: North Korea, North Hamgyong Province, Jipsam, shell length 20.0 mm, ZMFU no. 38634/Bv-5914; G, H – Protothaca (Novathaca) jedoensis (Lischke, 1874): North Korea, North Hamgyong Province, shell length 40.1 mm, ZMFU no. 38358/Bv-5774; I, J – Felaniella usta (Gould, 1861): North Korea, North Hamgyong Province, shell length 33.7 mm, ZMFU no. 38354/Bv-5770; K–N – Ruditapes philippinarium (A. Adams et Reeve, 1850): North Korea, North Hamgyong Province, Jipsam, shell length 18.6 mm, ZMFU no. 38618/Bv-5899; O–R – Gomphina (Macridiscus) melanaegis Römer, 1860: North Korea, North Hamgyong Province, Yombunjin, shell length 44.1 mm, ZMFU no. 38350/Bv-5766; S–V – Callista (Ezocallista) brevisiphonata (Carpenter, 1864): North Korea, North Hamgyong Province, Chongjin City, market, shell length 76.3 mm, ZMFU no. 38356/Bv-5772; W –Z – Protothaca (Protothaca) euglypta (Sowerby III, 1914): North Korea, North Hamgyong Province, shell length 42.1 mm, ZMFU no. 38359/Bv-5775.
Фототаблица 2 Plate 2 A, B – Crassostrea gigas (Thunberg, 1793): СевернаЯ КореЯ, провинциЯ Северный Хамгён, высота 91.3 мм, ЗМ ДВФУ № 38379/Bv-5785; C, D – Pododesmus (Monia) macrochisma (Deshayes, 1839): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 49.6 мм, ЗМ ДВФУ № 38635/Bv-5915; E, F – Mactra (Mactra) chinensis Philippi, 1846: СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 47.5 мм, ЗМ ДВФУ № 38353/Bv-5769; G, H – Spisula (Pseudocardium) sachalinensis (Schrenck, 1861): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 95.0 мм, ЗМ ДВФУ № 38355/Bv-5771; I, J – Mactromeris polynyma (Stimpson, 1860): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 45.6 мм, ЗМ ДВФУ № 38625/Bv-5905; K–N – Mizuhopecten yessoensis (Jay, 1857): СевернаЯ КореЯ, провинциЯ Северный Хамгён, г. ЧхондЖин, рынок, длина 65.9 мм, ЗМ ДВФУ № 38377/Bv-5783; O–P – Chlamys (Swiftopecten) swiftii (Bernardi, 1858): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 70.8 мм, ЗМ ДВФУ № 38378/Bv-5784. A, B – Crassostrea gigas (Thunberg, 1793): North Korea, North Hamgyong Province, shell height 91.3 mm, ZMFU no. 38379/Bv-5785; C, D – Pododesmus (Monia) macrochisma (Deshayes, 1839): North Korea, North Hamgyong Province, Jipsam, shell length 49.6 mm, ZMFU no. 38635/Bv-5915; E, F – Mactra (Mactra) chinensis Philippi, 1846: North Korea, North Hamgyong Province, shell length 47.5 mm, ZMFU no. 38353/Bv-5769; G, H – Spisula (Pseudocardium) sachalinensis (Schrenck, 1861): North Korea, North Hamgyong Province, shell length 95.0 mm, ZMFU no. 38355/Bv-5771; I, J – Mactromeris polynyma (Stimpson, 1860): North Korea, North Hamgyong Province, Jipsam, shell length 45.6 mm, ZMFU no. 38625/Bv-5905; K–N – Mizuhopecten yessoensis (Jay, 1857): North Korea, North Hamgyong Province, Chongjin City, market, shell length 65.9 mm, ZMFU no. 38377/Bv-5783; O–P – Chlamys (Swiftopecten) swiftii (Bernardi, 1858): North Korea, North Hamgyong Province, shell length 70.8 mm, ZMFU no. 38378/Bv-5784. in On the bivalve molluscan fauna of North Hamgyong Province (North Korea)
Фототаблица 2 Plate 2 A, B – Crassostrea gigas (Thunberg, 1793): СевернаЯ КореЯ, провинциЯ Северный Хамгён, высота 91.3 мм, ЗМ ДВФУ № 38379/Bv-5785; C, D – Pododesmus (Monia) macrochisma (Deshayes, 1839): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 49.6 мм, ЗМ ДВФУ № 38635/Bv-5915; E, F – Mactra (Mactra) chinensis Philippi, 1846: СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 47.5 мм, ЗМ ДВФУ № 38353/Bv-5769; G, H – Spisula (Pseudocardium) sachalinensis (Schrenck, 1861): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 95.0 мм, ЗМ ДВФУ № 38355/Bv-5771; I, J – Mactromeris polynyma (Stimpson, 1860): СевернаЯ КореЯ, провинциЯ Северный Хамгён, Чипсам, длина 45.6 мм, ЗМ ДВФУ № 38625/Bv-5905; K–N – Mizuhopecten yessoensis (Jay, 1857): СевернаЯ КореЯ, провинциЯ Северный Хамгён, г. ЧхондЖин, рынок, длина 65.9 мм, ЗМ ДВФУ № 38377/Bv-5783; O–P – Chlamys (Swiftopecten) swiftii (Bernardi, 1858): СевернаЯ КореЯ, провинциЯ Северный Хамгён, длина 70.8 мм, ЗМ ДВФУ № 38378/Bv-5784. A, B – Crassostrea gigas (Thunberg, 1793): North Korea, North Hamgyong Province, shell height 91.3 mm, ZMFU no. 38379/Bv-5785; C, D – Pododesmus (Monia) macrochisma (Deshayes, 1839): North Korea, North Hamgyong Province, Jipsam, shell length 49.6 mm, ZMFU no. 38635/Bv-5915; E, F – Mactra (Mactra) chinensis Philippi, 1846: North Korea, North Hamgyong Province, shell length 47.5 mm, ZMFU no. 38353/Bv-5769; G, H – Spisula (Pseudocardium) sachalinensis (Schrenck, 1861): North Korea, North Hamgyong Province, shell length 95.0 mm, ZMFU no. 38355/Bv-5771; I, J – Mactromeris polynyma (Stimpson, 1860): North Korea, North Hamgyong Province, Jipsam, shell length 45.6 mm, ZMFU no. 38625/Bv-5905; K–N – Mizuhopecten yessoensis (Jay, 1857): North Korea, North Hamgyong Province, Chongjin City, market, shell length 65.9 mm, ZMFU no. 38377/Bv-5783; O–P – Chlamys (Swiftopecten) swiftii (Bernardi, 1858): North Korea, North Hamgyong Province, shell length 70.8 mm, ZMFU no. 38378/Bv-5784.
Job-Related Health Issues That Affect Employees Working in Pharmaceutical Marketing
<p><strong><span>Abstract</span></strong></p> <p><span>Marketing representatives play a critical role in a pharmaceutical organization in the development and sustainability of their business through helping in selling of products and services. It is not an easy job. The Medical Marketing representative is a high-risk job with immense stress and negative consequences for individuals. It requires more skills, wider knowledge and emotional stability than the other profession. Due to extensive traveling, wandering and waiting time, target issues, work-life balancing problems and lack of job security the medical representative feels exhausted.</span></p> <p><span>In India, the pharmaceutical industry is growing tremendously for the past few years. This industry is highly competitive in nature. It increases the need for the marketing representatives and also their roles and responsibilities simultaneously. The cut-throat competitive scenario in the market increases the pressure of achieving targets to pharmaceutical sales which ultimately induced their job stress and other health issues. So, the pharmacy institution must provide more attention to prevent and reduce the burnout of the marketing representatives, otherwise the institution will lose its reputation.</span></p> <p><strong><span>Keywords: </span></strong><span>occupational risks; pharmaceutical marketing force; road traffic accidents; violence; workplace stress; Burnout; Frustrating; Medical Representatives; Stress and Work-Life Balancing Problems; stress management Violence; Well-being.</span></p>
Fig 3.4 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka
Fig 3.4: The A and B graph shows the number of Reef fish species and Shore and Inland fish species respectively vary with the season (Dec13 to May 14).
Fig 3.3 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka
Fig 3.3: Overall fish availability (%) in Urban (L)and Rural(R) markets, Here, Val-Valichennai, Odu-Oddumavadi, Bat-Batticaloa, Kal-Kaluvanchikuddy, Ara-Arampathy, Era-Eravur, Kat-Kattankuddy, Che-Chenkalady, Van-Vantharumoolai, Kir-Kiran, Kar-Kalar.
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.