Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports

Passenger travel information and generalized cost mean for each OD pair.

48 secondary school students participated in the experiment, all native speakers of Russian, mean age 15.4 years. Participants were rewarded with candies for successful rounds (see below). Participants were asked to learn an "alien" language and to communicate in it in pairs. During the learning stage the alien language was presented to the participants on a computer screen as a set of string-picture pairs. The members of a pair were trained on the same language: the first pair in a chain on the randomly generated one, and the following pairs on the output of the preceding pair (see below). The training took place simultaneously, but in different rooms. The meaning space was determined by three parameters: shape (four possible values); colour (two); background (three). The design was made asymmetrical (4x3x2) in order to keep the meaning space less predictable. The strings for the input language of the first pair were generated randomly in lowercase Cyrillic letters (represented in Roman transliteration here). After the learning stage, the communication stage took place. Two participants were seated in the same room at different sides of a screen, so that they could not see each other. The only thing they knew was that their partner was an "earthian" who had received the same training. Voice communication was forbidden. One of the participants (A) was shown a random picture on the screen of a laptop. She wrote a name for it on a paper card. The card was passed to the participant B who depicted on it a picture that, in her opinion, corresponded to that name. If the depiction coincided with the original stimulus, this round was considered successful and the p articipants were awarded a point. The original stimulus was then shown to B, and the card with the depiction was shown to A, so that they both knew their result. When writing a name for a figure, participants used a black pen, when drawing a picture, they could use any of four coloured pencils (red, blue, green, yellow). For the next round, the roles were exchanged, and the next picture was shown. Such rounds were held for all the 24 stimuli (presented in random order, each stimulus once). Thus, each of the participants performed 12 operations of naming a picture and 12 operations of drawing a picture for a given name. The communication system shared by A and B was considered to consist of the signs used in successful rounds. In other words, if the participants won a round, the name on the card was considered a word of their shared language, its meaning being the picture on the same card. This output language (E-language, as opposed to each participant's I-language) served as input for the next pair. If the number of signs in an E language (n) was larger than 12, then n-12 signs (chosen randomly) were removed. This rule was applied to the first input language as well. If n did not exceed 12, no bottleneck was applied. If n was smaller than 2, then language was considered extinct (evolutionary dead-end), since the pilot experiments showed that in this case the participants were unable to perform the communication task. Thus, the number of signs in the input languages was not fixed and could range from 2 to 12. Three chains (2, 4 and 5) ended up at the first round, since the pairs produced E language consisting of one sign only. This shows that the tasks offered to the participants were really difficult. Since the results of these pairs are not indicative of the cumulative evolution of language, they are not included into further analysis, which is thus limited to the successful chains. Three pairs struggled successfully through the first round, which resulted into three chains (1, 3 and 6) of seven generations each. The results are provided in the csv-file.

Collection description

Imaging phenotypes derived from multivariate linked ICA and CCA analysis of 449 preterm infants scanned at term-equivalent age. For details, see corresponding paper (doi: 10.1002/ana.24995).

Subject species

homo sapiens

Modality

Other

Cognitive paradigm (task)

None / Other

Map type

T

This Zenodo repository contains all migration flow estimates associated with the paper "Deep learning four decades of human migration." Evaluation code, training data, trained neural networks, and smaller flow datasets are available in the main GitHub repository, which also provides detailed instructions on data sourcing. Due to file size limits, the larger datasets are archived here.

Data is available in both NetCDF (.nc) and CSV (.csv) formats. The NetCDF format is more compact and pre-indexed, making it suitable for large files. In Python, datasets can be opened as xarray.Dataset objects, enabling coordinate-based data selection.

Each dataset uses the following coordinate conventions:

• Year: 1990–2023
• Birth ISO: Country of birth (UN ISO3)
• Origin ISO: Country of origin (UN ISO3)
• Destination ISO: Destination country (UN ISO3)
• Country ISO: Used for net migration data (UN ISO3)

The following data files are provided:

• T.nc: Full table of flows disaggregated by country of birth. Dimensions: Year, Birth ISO, Origin ISO, Destination ISO
• flows.nc: Total origin-destination flows (equivalent to T summed over Birth ISO). Dimensions: Year, Origin ISO, Destination ISO
• net_migration.nc: Net migration data by country. Dimensions: Year, Country ISO
• stocks.nc: Stock estimates for each country pair. Dimensions: Year, Origin ISO (corresponding to Birth ISO), Destination ISO
• test_flows.nc: Flow estimates on a randomly selected set of test edges, used for model validation

Additionally, two CSV files are provided for convenience:

• mig_unilateral.csv: Unilateral migration estimates per country, comprising:
  • imm: Total immigration flows
  • emi: Total emigration flows
  • net: Net migration
  • imm_pop: Total immigrant population (non-native-born)
  • emi_pop: Total emigrant population (living abroad)
• mig_bilateral.csv: Bilateral flow data, comprising:
  • mig_prev: Total origin-destination flows
  • mig_brth: Total birth-destination flows, where Origin ISO reflects place of birth

Each dataset includes a mean variable (mean estimate) and a std variable (standard deviation of the estimate).

An ISO3 conversion table is also provided.

This data collection contains diachronic semantic relatedness judgments for German word usage pairs. Find a description of the data format, code to process the data and further datasets on the WUGsite.

We provide additional data under misc/:

• testset: a semantic change test set with 22 German lexemes divided into two classes: lexemes for which the authors found

  1. innovative or
  2. reductive meaning change

  occurring in Deutsches Textarchiv (DTA) in the 19th century. Note that for some lexemes the change is already observable slightly before 1800 and some lexemes occur more than once in the test set (see paper). The columns 'earlier' and 'later' contain the mean of all judgments for the respective word. The columns 'delta_later' and 'compare' contain the predictions of the annotation-based measures of semantic change developed in the paper.

• tables: the full annotation table as annotators received it and a results table with rows in the same order. The columns 'date1' and 'date2' contain the date of the first and second use in the row. 'mean' contains the mean of all judgments for the use pair in this row without 0-judgments.
• plots: data visualization plots.

Please find more information on the provided data in the paper referenced below.

Version: 2.0.0, 30.9.2021.

Reference

Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann. 2018. Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT). New Orleans, Louisiana USA.

Summary of MA experiments. The two right-hand columns display the numbers of synonymous (Syn) and nonsynonymous (NSyn) base-pair substitutions (BPS) identified, respectively, while the mean and standard deviation of three replicates are presented. All strains except LQ had identical numbers of MA rounds among the three replicate MA lines, respectively. The column of Generations shows the number of elapsed generations estimated from the number of MA rounds and the recorded colony size of each round, while the mean and standard deviation of three replicates are presented. A list of all detected mutations and an extended table showing the number of intergenic BPS and short Indels are shown in S1 and S2 Tables, respectively.

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well.