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Supplemental material for "A phase 2a trial of brepocitinib for cicatricial alopecia"

Kurdish is an Indo-Iranian language that is largely spoken by people of Kurdish descent in the countries of Turkey, Iraq, Iran, and Syria. It contains a number of regional dialects, the most common of which is Northern Kurdish (also known as Kurmanji or Badini), while Central Kurdish (also known as Sorani) is spoken in some regions of Iraq and Iran. A unique Kurdish dialect, Hawrami, often referred to as Gorani, is the primary language spoken in the Hawraman area, which spans portions of western Iran and northeastern Iraq. Despite the fact that the dialects have separate pronunciations, vocabularies, and certain grammatical distinctions, they share a common core. In spite of the difficulties, the Kurdish language continues to be an essential component of Kurdish identity and cultural legacy. It plays an essential role in the protection and promotion of the distinct cultural identity of the Kurdish people. The concepts of language and dialect recognition are intricately interconnected within the fields of linguistics and natural language processing. Having a good dataset for Kurdish dialect recognition improves identification and classification, natural language processing applications for Kurdish, preservation of Kurdish linguistic heritage, cultural insights, customized content and services for users, empowerment of local businesses, and a benchmark for evaluating dialect recognition systems. The presented dataset was gathered by numerous members of the University of Halabja's Computer Science Department's teaching staff over the course of several months. During each stage of the data collecting process, the established policies, procedures, and guidelines were adhered to. This included taking into consideration the ages as well as the genders of the speakers who were included in the dataset. The recordings are taken from a variety of TV programmes and TV interviews that were broadcast on Speda tv, NRT, and GK Sat. There were 2000 instances of the Sorani dialect, 2000 examples of the Badini dialect, and 2000 examples of the Hawrami dialect. The total duration of this dataset is 6000 s, and the duration of each sample is precisely one second. The dataset labeling procedure that has been suggested consists of two consecutive stages. Initially, it is necessary to categorize the distinct sounds of each dialect, namely Sorani, Badini, and Hawrami, into different directories. Following this, it is recommended that the files included inside these folders be systematically labeled from 1 to 2000, according to the prescribed scheme: for Sorani files, the labels should range from s1 to s2000; for Badini files, the labels should range from b1 to b2000; and for Hawrami files, the labels should range from h1 to h2000.

Our research team from the Computer Science Department at the University of Halabja has developed an innovative dataset collection named the Multi-Keyboard Acoustic (MKA) Datasets. The Multi-Keyboard Acoustic (MKA) Datasets, designed to aid in keyboard sound recognition and analysis, address the critical need for defending against acoustic-based cyber threats. With the increasing sophistication of cyberattacks, focusing on keyboard acoustics is particularly timely. The MKA Datasets encompass detailed recordings from six commonly used platforms: HP, Lenovo, MSI, Mac, Messenger, and Zoom. Each platform's dataset includes raw recordings, segmented sound files, and matrices derived from these sounds, capturing the subtle variations in typing behavior across different devices and applications. We meticulously organize the MKA datasets to facilitate ease of use and thorough analysis. Each platform has a dedicated folder containing subfolders for raw data, segmented sound files, and matrices. Additionally, an aggregated folder combines data from all platforms, providing a broad spectrum for cross-platform analysis. In total, the MKA datasets consist of around 2630 files with.wav extensions for sound segments, as well as an equal number of matrix and.txt files. The number of files varies by platform, with approximately 70 files for HP, Lenovo, MSI, Zoom, and Messenger, and 61 files for Mac. Within each platform's dataset, the "Sound segments" folder stores six one-second WAV audio excerpts derived from the corresponding raw data files for each class, renamed using a convention of "class_name+1" to "class_name+6" for each platform individually and "class_name+platform_name1" to "class_name+platform_name6" for the aggregated datasets. The "Sound segment (.matrix)" folder contains feature representations, such as MFCCs, extracted from each sound segment. Additionally, the "Sound segment metadata (.txt)" folder holds detailed information for each sound segment, including recording conditions, platform information, and keystroke class labels. Beyond cybersecurity, the MKA datasets have potential applications in domains such as speech recognition and natural language processing. The datasets, which provide a diverse set of sound profiles, support the development of more robust and adaptable algorithms in these fields. The versatility of the MKA datasets makes them an invaluable tool not only for advancing cybersecurity research, but also for improving the efficiency and accuracy of human-computer interaction technologies. Through our comprehensive approach, we aim to contribute significantly to both academic research and practical applications in these interconnected areas.

Monthly data on volatility of the forex market and domestic economic variables.

Supplementary data

NYC rent burden based on New York City Housing and Vacancy Survey (2002 ~ 2017), triennial

Sequence file, results of phylogenetic analysis and molecular docking.

The data is too large, here is a downloadable synopsis of the data, detailed data is here https://portal.gdc.cancer.gov/projects/TCGA-STAD

The original data show the concentrations of PAEs detected in the soil and agricultural products of facilities in Xinjiang, among which 249 samples of soil and 203 samples of agricultural products were detected, and 5 compounds were detected. The concentrations of PAEs in 6 types of agricultural products were also displayed.

The research hypothesis is that frequent use of social media contributes to increased anxiety, depression, and loneliness among young adults. This hypothesis is supported by the data, which shows that more frequent social media users are more likely to report experiencing mental health problems. The data also shows that excessive social media use is associated with higher levels of reported loneliness. The data was gathered through a review of existing research on the topic. The researchers searched academic databases, grey literature, and online platforms for studies that investigated the relationship between social media use and mental health in young adults. They then screened and selected studies that met specific criteria, such as those that focused on young adults, used peer-reviewed methods, and were published within the last five years. The researchers then extracted and analyzed data from the selected studies. The data shows that there is a strong association between social media use and mental health problems. The researchers found that more frequent social media users are more likely to report experiencing anxiety, depression, and loneliness. They also found that excessive social media use is associated with higher levels of reported loneliness. The data can be interpreted in a number of ways. One interpretation is that social media can have a negative impact on mental health. This is likely due to a number of factors, such as the pressure to present a perfect image online, the constant exposure to negative or upsetting content, and the tendency to compare oneself to others. Another interpretation is that social media can be a useful tool for connecting with others and staying informed, but it is important to use it in moderation and to be aware of its potential negative effects. The data can be used to inform the development of interventions and support systems for young adults who are struggling with mental health problems. For example, the data could be used to develop educational programs that teach young adults about the potential negative effects of social media and how to use it in a healthy way. The data could also be used to develop support groups for young adults who are struggling with anxiety, depression, or loneliness. It is important to note that the data presented in this research is correlational, which means that it does not prove that social media causes mental health problems. However, the data does suggest that there is a strong association between social media use and mental health problems. Further research is needed to determine whether social media use is a cause or a consequence of mental health problems.