A dynamic repository featuring over 1,500 fictional teachers from more than 700 films and television programs. It is continually updated with comprehensive profiles of the teachers, school contexts, and text metadata.

Raw data of the CRISPR screen results published in Nature Biomedical Engineering entitled "Identification of druggable regulators of cell secretion via a kinome-wide screen and high-throughput immunomagnetic cell sorting"

The Northeast Fisheries Observer Database System (OBDBS) contains data collected on commercial fishing vessels by observers from 1989 - present and at-sea monitors from 2010 - present. The data include detailed gear, effort, catch, bycatch of finfish and protected species as well as biological samples of priority and protected species. Data is collected by trained fishery observers and at-sea monitors for scientific and fisheries management purposes.

This project aims to automate the detection of cracks in smartphone screens to ensure fairness and standardization in the used phone market. It employs a Convolutional Neural Network (CNN) model trained on a custom image dataset to classify screens as cracked or non-cracked, achieving an accuracy of 92%. Unlike traditional manual inspection methods, the CNN-based approach offers consistent, objective, and scalable evaluation. Challenges include detecting rare crack patterns, handling variable lighting conditions, and mitigating overfitting due to limited data. Future improvements will explore advanced architectures and data augmentation techniques to enhance model robustness and generalizability.

This is human 3D skeleton data files that is part of the dataset found in the collection at:https://doi.org/10.25452/figshare.plus.c.5774969

This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7

Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.

This is a monitor using data from Swift/BAT, MAXI and Fermi/GBM instruments. Click on the table header to change the sorting behaviour. Color code for the probability: (rising flux , no change, decreasing flux) The average flux is calculated as the average of the last 3 days of data, without taking the errors into account. The fermi flux is not given in mCrab. Please note that the accuracy of the probabilities is limited by the accuracy of the data used. Release of Friday, the 17th of October 2014 Last updated at 14:04:07 UT Created as an ESAC trainee project by Eva Laplace. Supervisors: Peter Kretschmar, Emilio Salazar and Miguel Arregui. Contact: peter.kretschmar@esa.int

Comprehensive dataset of 6,294 Screen repair services in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The list of human proteins in reviewed entries obtained from UniprotKB was searched in the O-GlcNAc database (oglcnac.com)

93554 Global export shipment records of Screen monitor with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The global monitor privacy film market is projected to reach $1342.5 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 3.0% from 2025 to 2033. This steady growth reflects increasing concerns about data security and visual privacy in both professional and home office settings. Drivers for this market include the rising adoption of remote work, increasing cybersecurity threats, and growing awareness of data breaches. The market is segmented by screen size (11.6 inches to 15.6 inches and others), sales channel (online and offline), and geographic region. The increasing popularity of larger monitors and laptops, especially in the 15.6-inch category, is fueling demand for larger privacy films. The online sales channel is expected to dominate due to the convenience and wider reach offered by e-commerce platforms. Geographically, North America and Europe are currently leading the market, driven by high technology adoption and stringent data privacy regulations. However, rapid economic growth and increasing digitalization in regions like Asia-Pacific are expected to contribute significantly to market expansion in the coming years. Competition is relatively high, with several established players and emerging brands vying for market share. The market is characterized by continuous product innovation, with manufacturers focusing on improving film quality, durability, and ease of application. The sustained growth of the monitor privacy film market is expected to continue throughout the forecast period. Factors such as the increasing adoption of hybrid work models, the growing popularity of co-working spaces, and enhanced focus on workplace security will contribute to this upward trajectory. While restraints like the relatively higher cost compared to standard screen protectors exist, the growing perception of privacy as a valuable asset is outweighing this factor. Continued technological advancements, such as the development of anti-glare and anti-fingerprint features integrated into privacy films, will further enhance market appeal and drive growth. Market players are also focusing on strategic partnerships and collaborations to expand their reach and strengthen their market positions. The ongoing evolution of cybersecurity threats and the increasing vulnerability of sensitive data will continue to fuel demand for reliable and effective monitor privacy solutions.

288 Global import shipment records of Screen Panel with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

China EP: Production: Electronic Screen data was reported at 3,930.258 Unit th in 2006. This records an increase from the previous number of 86.660 Unit th for 2005. China EP: Production: Electronic Screen data is updated yearly, averaging 40.516 Unit th from Dec 2000 (Median) to 2006, with 7 observations. The data reached an all-time high of 3,930.258 Unit th in 2006 and a record low of 0.006 Unit th in 2000. China EP: Production: Electronic Screen data remains active status in CEIC and is reported by Ministry of Industry and Information Technology. The data is categorized under Global Database’s China – Table CN.RFA: Electronic Product Production: Annual.

Comprehensive dataset of 987 Screen printing supply stores in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Percentage of cues pecked and latency to peck data for screen-peck task 1 (no affect manipulation). Data are means where birds see 17 P cues, 2 NP cues, 2 M cues, 1 NN cues and 17 N cues per session. To calculate an average measure, the total latency (counted as 10s if the cue was not pecked) was divided by the number of trials in which a peck occurred. P and N latencies were only available for the last 3 sessions (percenatge pecked data was available for all 6 sessions).

Latencies (seconds) for pecking each cue in the hot vs cold condition. Raw data is shown for each session. Where a cue was not pecked it was given a value of 10s and to calculate an average measure, the total latency was divided by the number of trials in which a peck of that cue type actually occurred. Red formatting signifies hot condition and blue formatting signifies cold condition.

We surveyed 1,257 12- to 18-year-old adolescents attending 52 schools in urban or suburban areas of Argentina. We recorded the daily exposure to various screen-based activities, including video- and online-gaming, social media, TV or streaming. Screen time and device type in the hour before bedtime, sleep patterns during weekdays and weekends, somnolence (Pediatric Daytime Sleepiness Scale score), and grades in language and mathematics were also assessed.

Korea CI Sales: Animation: Screen Theater data was reported at 149,162.000 KRW mn in 2015. This records a decrease from the previous number of 151,908.000 KRW mn for 2014. Korea CI Sales: Animation: Screen Theater data is updated yearly, averaging 69,052.000 KRW mn from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 151,908.000 KRW mn in 2014 and a record low of 11,564.000 KRW mn in 2003. Korea CI Sales: Animation: Screen Theater data remains active status in CEIC and is reported by Korea Creative Content Agency. The data is categorized under Global Database’s Korea – Table KR.H087: Contents Industry Sales: by Industry.

We compiled a large cohort of breast cancer samples from NCBI's Gene Expression Omnibus (GEO) (see Table 1) as it was suggested in (Györffy and Schäfer, 2009). We only took samples from the U133A platform into account and removed duplicate samples, that is, samples that occur in several studies under the same GEO id. Array quality checks were executed for all samples belonging to the same study by the R packagearrayQualityMetrics. Due to high memory demands of this package, studies containing more than 400 samples had to be divided into two parts. Samples that were classified as outliers in the RLE or NUSE analysis were discarded. Finally, all samples across all studies were normalized together using R's justRMA function yielding for each sample and each probe a log(intensity) value. This normalization also included a quantile normalization step. Subsequently, probe intensities were mean centered, yielding for each sample and each probep a log(intensityμ(intensityp))log(intensityμ(intensityp)) value.We found batch effects within single studies, where samples have been collected from different locations and batch effects between studies. Specifically for breast cancer, samples also form batches according to the five subtypes of breast cancer: luminal A, luminal B, Her2 enriched, normal like and basal like. To account for these effects we employed R's combat, where the cancer subtype was modeled as an additional covariate to maintain the variance associated with the subtypes. To do so we needed to stratify the patients according to the subtype. Since this variable is not always available in the annotation of the patients, we predict the subtype employing the PAM50 marker genes as documented in R's genefu package.Principal component analysis of the batch corrected data revealed pairs of samples with a very high correlation (>0.9). Those pairs were regarded as replicate samples. For each pair of replicate samples one sample was removed randomly. Affymetrix probe IDs were mapped to Entrez Gene IDs via the mapping files provided by Affymetrix. Only probes that mapped to exactly one Gene ID were taken into account and probes starting with AFFX were discarded. If an Entrez Gene ID mapped to several Affymetrix probe IDs, probes were considered in the following order according to their suffix (Gohlmann and Talloen, 2010): “_at,” “s_at,” “x_at,” “i_at,” and “a_at.” When there were still several probes valid for one Gene ID, the Affymetrix probe with the higher variance of expression values was chosen.The patients' class labels corresponding to recurrence free or distant metastasis free survival were calculated with respect to a 5-year threshold. The final cohort is shown in Table 1. We derived two data sets: one labeled according to recurrence free survival (RFS) and one labeled according to distant metastasis free survival (DMFS). Note, that the DMFS data set is a subset of the RFS data set.

Percentage pecked data for the screen-peck task with affect manipulation. Data show the percentage of each cue type pecked where birds are presented with 17 P cues, 2 NP cues, 2 M cues, 2 NN cues and 17 N cues per session. Red formatting signifies hot condition and blue formatting signifies cold condition.

These files are a static database dump of the database presented in:

Woods-Robinson, R., Horton, M. K., & Persson, K. A. (2022). A method to computationally screen for tunable properties of crystalline alloys. arXiv preprint arXiv:2206.10715.

The database presented in this work is intended to be a living resource, and will be updated and revised over time. The files here are made available only a static snapshot to store a record of the latest version of the database available at the point in time that the manuscript was finalized. It is expected that the latest version will be available at Materials Project and users are strongly advised to retrieve the latest data when performing any follow-up work. This data will be available from the Materials Project API, https://materialsproject.org/api, and will be the most up-to-date, incorporating any new database additions or changes, and the latest bug fixes (where applicable).

The files in this snapshot can be loaded via either Python, using the monty package and its loadfn function, or via other JSON parsing libraries, or can be imported directly in MongoDB or similar database system. It is advisable to have the pymatgen-analysis-alloys package installed via pip or otherwise to make use of this data, version 0.0.6 or greater. Note that duplicates of the respective AlloyPair objects are not included within the AlloySystem records for reasons of space, but the relevant the pair_id identifiers are included, and these objects can be fully reconstructed as necessary from the data stored within the alloy_pairs.json.gz file.

Please refer any questions to the authors.