Countdown API provides reliable, real-time eBay product, category, reviews, offers, and search results data from any eBay domain. The API includes comprehensive coverage of each of the search results in a cleanly structured output.

You can select any eBay domain worldwide for your search and can use the customer location request parameter to see details of how a product appears on a given eBay domain to a customer located in a different country than that of the chosen eBay domain (product results). For search results, you can specify several other parameters to tailor a search: category ID, listing type, sort by options, condition, and more. Countdown APIs high-capacity, global infrastructure assures you the highest level of performance and reliability. For easy integration with your eBay data apps and services, data is delivered in JSON or CSV format.

Data is retrieved by search term, or for single products, by the EPID (eBay product identifier) or by global identifiers GTIN/ISBN/UPC/EAN – you can request to have these converted to eBay EPIDs. Alternatively, you may request to search by an eBay product-page or search results page URL.

So what's in the data from Countdown API?

Product: - Master Page: shows a summary of all listings for a known product summerized in the top_picks
array - Individual Listing Page: detailed product listing - Product details: title, description, brand, attributes, categories, images, last updated, etc. - Returns policy - Stock status - Condition - Auction details (i.e. "time left") - Offer - Payment methods - Seller details - Promotion ("why buy") - Shipping details - Reviews

Search Results: - Position - Title - EPID - Link - Image - "Hotness" (i.e. "500+ sold") - Condition - Auction vs. Buy it now - If offers free returns - Ended: date, if sold - Any available search facets (refinements) shown on the search result page - Search information object: original search term vs. "did you mean" or spelling correction - Pagination details

How can Countdown API be used?

• Product listing management
• Price monitoring
• Category & product trends monitoring
• Market research & competitor intelligence
• Cross-border shipping data
• Rank tracking on eBay

Who uses Countdown API? This data is leveraged by software developers, marketers & business owners, sales & business development teams, researchers, and data analysts & engineers, in ecommerce, other retail business and solutions (dropshipping/logistics), and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

UNICEF's country profile for Iraq , including under-five mortality rates, child health, education and sanitation data.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

b'SPM{T_[291.0]} - contrast 2: Negative'

Collection description

Keywords: fear and anxiety; bed nucleus of the stria terminalis (BST/BNST); central nucleus of the amygdala (Ce/CeA); central extended amygdala (EAc)

Nomenclature: CS - certain safety anticipation, CT - certain threat anticipation, US - uncertain safety anticipation, UT - uncertain threat anticipation

OSF Resource Sharing - https://osf.io/fcvdj/ (DOI 10.17605/OSF.IO/FCVDJ)

MEGA-ANALYSIS METHOD
Overview of the Mega-Analysis
The neuroimaging mega-analysis capitalized on data from two previously published fMRI studies focused on the neural circuits recruited by certain and uncertain threat-anticipation. The first study encompassed a sample of 220 psychiatrically healthy, first-year university students (Hur et al., 2022). The second study encompassed a mixed campus/community sample of 75 tobacco smokers (Kim et al., 2023). Both studies employed the same threat-anticipation paradigm (Maryland Threat Countdown task) and were collected using identical parameters on the same scanner. For the mega-analysis, all of the neuroimaging data were completely reprocessed using the identical pipeline, as detailed below. All participants provided informed written consent. Procedures were approved by the University of Maryland, College Park Institutional Review Board (protocols #659385 and #824438).
Detailed descriptions of the study designs, enrollment criteria, participants, data collection procedures, and data exclusions are provided in the original reports (Hur et al., 2022; Kim et al., 2023). The mega-analysis was not pre-registered.
Participants
Across studies, an ethnoracially diverse sample of 295 participants provided usable neuroimaging data (45.4% female; 52.2% White Nonhispanic, 16.6% Asian, 19.0% African American, 4.1% Hispanic, 8.1% Multiracial/Other; M=21.6 years, SD=5.7).
Power Analysis
To enable readers to better gauge their confidence in our results, we performed a post hoc power analysis. G-power (version 3.1.9.2) indicated that the final sample of 295 usable fMRI datasets provides 80% power to detect “small” mean differences in region-of-interest activation (1-df contrasts, Cohen’s d=0.16, α=0.05, two-tailed) (Cohen, 1988; Faul et al., 2007).
Threat-Anticipation Paradigm
Paradigm Structure and Design Considerations. The Maryland Threat Countdown paradigm is a well-established, fMRI-optimized variant of temporally uncertain-threat assays that have been validated using fear-potentiated startle and acute anxiolytic administration (e.g., benzodiazepine) in mice, rats, and humans (Daldrup et al., 2015; Hefner et al., 2013; Lange et al., 2017; Miles et al., 2011; Moberg et al., 2017). The paradigm has been successfully used in several prior fMRI studies (Grogans et al., 2023; Hur et al., 2022; Hur et al., 2020; Kim et al., 2023).
The paradigm takes the form of a 2 (Valence: Threat/Safety) × 2 (Temporal Certainty: Uncertain/Certain) randomized, event-related, repeated-measures design (3 scans; 6 trials/condition/scan). Participants were completely informed about the task design and contingencies prior to scanning. Simulations were used to optimize the detection and deconvolution of task-related hemodynamic signals. Stimulus presentation was controlled using Presentation software (version 19.0, Neurobehavioral Systems, Berkeley, CA).
On Certain-Threat trials, participants saw a descending stream of integers (‘count-down;’ e.g., 30, 29, 28...3, 2, 1) for 18.75 s. To ensure robust distress, this anticipation epoch culminated with the presentation of a noxious electric shock, unpleasant photograph (e.g., mutilated body), and thematically related audio clip (e.g., scream, gunshot). Uncertain-Threat trials were similar, but the integer stream was randomized and presented for an uncertain and variable duration (8.75-30.00 s; M=18.75 s). Participants knew that something aversive was going to occur, but had no way of knowing precisely when. Consistent with recent recommendations (Shackman & Fox, 2016), the average duration of the anticipation epoch was identical across conditions, ensuring an equal number of measurements (TRs/condition). The specific mean duration was chosen to enhance detection of task-related differences in the blood oxygen level-dependent (BOLD) signal (‘activation’) (Henson, 2007) and to allow sufficient time for sustained responses to become evident. Safety trials were similar, but terminated with the delivery of benign reinforcers (see below). Valence was continuously signaled during the anticipation epoch (‘countdown’) by the background color of the display. Temporal certainty was signaled by the nature of the integer stream. Certain trials always began with the presentation of the number 30. On Uncertain trials, integers were randomly drawn from a near-uniform distribution ranging from 1 to 45 to reinforce the impression that they could be much shorter or longer than Certain trials and to minimize incidental temporal learning (‘time-keeping’). To concretely demonstrate the variable duration of Uncertain trials, during scanning, the first three Uncertain trials featured short (8.75 s), medium (15.00 s), and long (28.75 s) anticipation epochs. To mitigate potential confusion and eliminate mnemonic demands, a lower-case ‘c’ or ‘u’ was presented at the lower edge of the display throughout the anticipatory epoch. White-noise visual masks (3.2 s) were presented between trials to minimize the persistence of visual reinforcers in iconic memory.
Participants were periodically prompted (following the offset of the white-noise visual mask) to rate the intensity of fear/anxiety experienced a few seconds earlier, during the anticipation (‘countdown’) period of the prior trial, using a 1 (minimal) to 4 (maximal) scale and an MRI-compatible response pad (MRA, Washington, PA). Each condition was rated once per scan (16.7% trials). Skin conductance was continuously acquired throughout.
Procedures. Prior to scanning, participants practiced an abbreviated version of the paradigm (without electrical stimulation) until they indicated and staff confirmed understanding. Benign and aversive electrical stimulation levels were individually titrated. Benign Stimulation. Participants were asked whether they could “reliably detect” a 20 V stimulus and whether it was “at all unpleasant.” If the participant could not detect the stimulus, the voltage was increased by 4 V and the process repeated. If the participant indicated that the stimulus was unpleasant, the voltage was reduced by 4V and the process was repeated. The final level chosen served as the benign electrical stimulation during the imaging assessment. Aversive Stimulation. Participants received a 100 V stimulus and were asked whether it was “as unpleasant as you are willing to tolerate”—an instruction specifically chosen to maximize anxious distress and arousal. If the participant indicated that they were willing to tolerate more intense stimulation, the voltage was increased by 10 V and the process repeated. If the participant indicated that the stimulus was too intense, the voltage was reduced by 5 V and the process repeated. The final level chosen served as the aversive electrical stimulation during the imaging assessment. Following each scan, staff re-assessed whether stimulation was sufficiently intense and increased the level as necessary.
Electrical Stimuli. Electrical stimuli (100 ms; 2 ms pulses every 10 ms) were generated using an MRI-compatible constant-voltage stimulator system (STMEPM-MRI; Biopac Systems, Inc., Goleta, CA) and delivered using MRI-compatible, disposable carbon electrodes (Biopac) attached to the fourth and fifth digits of the non-dominant hand.
Visual Stimuli. Seventy-two aversive and benign photographs (1.8 s) were selected from the International Affective Picture System (for details, see Hur et al., 2020). Visual stimuli were back-projected (Powerlite Pro G5550, Epson America, Inc., Long Beach, CA) onto a semi-opaque screen mounted at the head-end of the scanner bore and viewed using a mirror mounted on the head-coil.
Auditory Stimuli. Seventy-two aversive and benign auditory stimuli (0.8 s) were adapted from open-access online sources and delivered using an amplifier (PA-1 Whirlwind) with in-line noise-reducing filters and ear buds (S14; Sensimetrics, Gloucester, MA) fitted with noise-reducing ear plugs (Hearing Components, Inc., St. Paul, MN).
MRI Data Acquisition
Data were acquired using a Siemens Magnetom TIM Trio 3 Tesla scanner (32-channel head-coil). Foam inserts were used to immobilize the participant’s head within the head-coil. Participants were continuously monitored using an eye-tracker (Eyelink 1000; SR Research, Ottawa, Ontario, Canada) and the AFNI real-time motion plugin (Cox, 1996). Eye-tracking data were not recorded. Sagittal T1-weighted anatomical images were acquired using a magnetization prepared rapid acquisition gradient echo sequence (TR=2,400 ms; TE=2.01 ms; inversion time=1,060 ms; flip=8°; slice thickness=0.8 mm; in-plane=0.8×0.8 mm; matrix=300×320; field-of-view=240×256). A T2-weighted image was collected co-planar to the T1-weighted image (TR=3,200 ms; TE=564 ms; flip angle=120°). To enhance resolution, a multi-band sequence was used to collect oblique-axial echo-planar imaging (EPI) volumes (multiband acceleration=6; TR=1,250 ms; TE=39.4 ms; flip=36.4°; slice thickness=2.2 mm, number of slices=60; in-plane resolution=2.1875×2.1875 mm; matrix=96×96). Data were collected in the oblique-axial plane (approximately −20° relative to the AC-PC plane) to minimize susceptibility artifacts. Three 478-volume EPI scans were acquired. The scanner automatically discarded the first 7 volumes. To enable fieldmap correction, two oblique-axial spin echo (SE) images were collected in opposing

The Traffic Light Countdown Timer market is experiencing a significant transformation, driven by a growing emphasis on road safety, traffic management, and smart city initiatives. Traffic light countdown timers are innovative devices that display the remaining time for a traffic light change, allowing drivers and pe

Objective: Rear-end accidents are the most common accident type at signalized intersections, because the diversity of actions taken increases due to signal change. Green signal countdown devices (GSCDs), which have been widely installed in Asia, are thought to have the potential of improving capacity and reducing accidents, but some negative effects on intersection safety have been observed in practice; for example, an increase in rear-end accidents.Methods: A microscopic modeling approach was applied to estimate rear-end accident probability during the phase transition interval in the study. The rear-end accident probability is determined by the following probabilities: (1) a leading vehicle makes a “stop” decision, which was formulated by using a binary logistic model, and (2) the following vehicle fails to stop in the available stopping distance, which is closely related to the critical deceleration used by the leading vehicle. Based on the field observation carried out at 2 GSCD intersections and 2 NGSCD intersections (i.e., intersections without GSCD devices) along an arterial in Suzhou, the rear-end probabilities at GSCD and NGSCD intersections were calculated using Monte Carlo simulation.Results: The results suggested that, on the one hand, GSCDs caused significantly negative safety effects during the flashing green interval, especially for vehicles in a zone ranging from 15 to 70 m; on the other hand, GSCD devices were helpful in reducing rear-end accidents during the yellow interval, especially in a zone from 0 to 50 m.Conclusions: GSCDs helped shorten indecision zones and reduce rear-end collisions near the stop line during the yellow interval, but they easily resulted in risky car following behavior and much higher rear-end collision probabilities at indecision zones during both flashing green and yellow intervals. GSCDs are recommended to be cautiously installed and education on safe driving behavior should be available.

The Traffic Signal Countdown Timer market has emerged as a vital segment of urban transportation management, enhancing road safety and efficiency in traffic flow. These countdown timers are designed to display precise countdowns for traffic lights, providing drivers and pedestrians with crucial information on how mu

UNICEF's country profile for Uganda , including under-five mortality rates, child health, education and sanitation data.

UNICEF's country profile for Peru , including under-five mortality rates, child health, education and sanitation data.

Sectors on determining child survival in the CRS database.

UNICEF's country profile for Gabon , including under-five mortality rates, child health, education and sanitation data.

In January 2025, the Doomsday Clock was moved one second closer to midnight, to 89 seconds - this is the closest the hand has been to midnight since the clock was created in 1947. The clock is a metaphor of how close humanity is to destroying itself, with midnight representing the end of human civilization. The hand's placement originally depended on how close the world was to nuclear annihilation, taking factors such as conflict and political instability into account, however it has also included the threat of climate change since 2007. The clock's hand is set annually by members of the Bulletin of the Atomic Sciences, and its position can move closer to or further from midnight, depending on the current state of global affairs. Reasons for the most recent move include the war in Ukraine, global pandemics, and the continued perceived inaction in the face of climate change.

This repository contains all replication materials for the paper titled "Authoritarianism Rising as Polls Loom: The Authoritarian Election Countdown Effect". The study investigates the relationship between electoral threat and authoritarian rhetoric in Slovak parliamentary debates over the past 30 years. It includes the cleaned data, analysis scripts, and documentation necessary to reproduce the results and figures presented in the paper.

UNICEF's country profile for Mali , including under-five mortality rates, child health, education and sanitation data.

Ike Countdown to D Day II Dünya Savaşı nın dönüm noktası olan D Day çıkarma Günü ve bu muharebenin önemli komutanı Dwigh

Shows a real-time departure countdown for the Vienna public transport network (Wiener Linien). Minimize the waiting time at bus / tram / subway stops by knowing when to leave your apartment and getting there just in time! The little app runs on the Raspberry Pi and uses the real-time departure service of the Vienna public transport network (Wiener Linien) to show the countdown to the next departures for two different lines at your closest station. Features: Shows the next two departures for two different public transport stops / lines Refreshes countdown every 20 seconds Auto-starts when booting your Raspberry Pi Configurable through command line parameters Shows live data in green, errors in red Uses open data from the City of Vienna. Code is based on WL-Monitor-Pi from Matthias Bendel / mabe-at and released under the same MIT license - thanks for creating the original project and inspiring this adaption!

UNICEF's country profile for Haiti , including under-five mortality rates, child health, education and sanitation data.

This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.

The Parliament’s Spring 2024 Eurobarometer reveals strong interest among citizens in the upcoming European elections (6-9 June) and awareness of their significance in the current geopolitical context. The survey sheds light on Europeans’ voting behaviour, their attitudes towards campaign topics as well as on preferences for the priority values for the next term of the European Parliament. It focuses also on citizens’ perception of the EP and EU, on their perspective on life in the EU, as well as on their opinions about the EU within the current global context.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
• Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Regional trends in the Countdown maternal, newborn and child health coverage score (%), based on demographic and health survey data 1991–2010, with average annual rate of relative change of the coverage rate during the whole period (1991–2010) and between the last two surveys (2004–2010).