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Mazkur maqolada Shukur Xolmirzaev asarlarida xalqona va falsafiy tilning lingvopoetik xususiyatlari tahlil qilinadi. Yozuvchining hikoya, qissa va romanlarida xalqona leksika, maqol va matallar, og'za…

Mazkur maqolada Shukur Xolmirzaev asarlarida xalqona va falsafiy tilning lingvopoetik xususiyatlari tahlil qilinadi. Yozuvchining hikoya, qissa va romanlarida xalqona leksika, maqol va matallar, og'zaki nutq unsurlari, milliy mentalitet hamda falsafiy mushohadalarning badiiy ifodasi yoritiladi. Tadqiqotda xalqona tilning qahramon xarakterini ochish, psixologik tasvirni kuchaytirish, milliy koloritni yaratish va voqelikni realizm asosida aks ettirishdagi o'rni ko'rsatib beriladi. Shuningdek, Xolmirzaev ijodida falsafiy nutqning xalqona tafakkur bilan uyg'unlashuvi, ichki monolog, sukut poetikasi va tabiat tasvirlari orqali inson ruhiyati hamda mavjudlik masalalarining badiiy talqini tahlil etiladi. Muallifning lingvopoetik mahorati xalqona va falsafiy qatlamlarning uzviy birligi asosida zamonaviy o'zbek nasrining estetik xususiyatlarini namoyon etishi bilan izohlanadi.

Replication package for an event study of how Borsa Istanbul prices firms' dependence on government procurement across nine oppositely-signed shifts in incumbent political power (2015-2024). Contains …

Replication package for an event study of how Borsa Istanbul prices firms' dependence on government procurement across nine oppositely-signed shifts in incumbent political power (2015-2024). Contains the hand-coded treatment data (treatment_tags.csv, with per-firm sources), the screened firm universe, daily price data, and all Python code reproducing every table and figure in the paper. See README.md and data/DATA_README.md.

Scientific creativity means the ability to learn scientific knowledge and to solve scientific problems. It is one of the parts of creativity. Somehow scientific creativity of school students depends o…

Scientific creativity means the ability to learn scientific knowledge and to solve scientific problems. It is one of the parts of creativity. Somehow scientific creativity of school students depends on their type of personality. Some of the students are introvert while some are extrovert. A kind of student who is mostly quiet and doesn’t open easily is said to be an introvert. In contrast, when a student is social, communicative and quickly establish friendship, is considered to have   extrovert personality. This research aimed to find the comparison between the scientific creativity of introverted students and extroverted students.  

SQLite-LAP SQLite-LAP prefetches the corresponding leaf pages identified by the Final Interior Node, enabling non-blocking and parallel processing of I/O requests to maximize system throughput and re…

SQLite-LAP SQLite-LAP prefetches the corresponding leaf pages identified by the Final Interior Node, enabling non-blocking and parallel processing of I/O requests to maximize system throughput and resource utilization. SQLite-LAP fur- ther optimizes data access by enabling concurrent prefetching, thereby maximizing cache utilization and reducing query latency. See details in our EMSOFT 2026 paper: Dohwan Lee, Yewon Shin, Wook-Hee Kim, Jonghyeok Park. "Lightweight Asynchronous I/O Optimization for SQLite Scan Performance on Embedded Devices". EMSOFT 2026   Features FIN-aware Asynchronous Prefetching: When a scan reaches a Final Interior Node (FIN), a background thread prefetches all leaf pages under that FIN via io_uring, decoupling disk I/O from query execution. I/O Parallelism via Batched Submission: io_uring enables batched read submission and kernel-level polling (SQPOLL), reducing per-request system call overhead compared to synchronous I/O. Minimal Changes to SQLite: The FIN table and prefetcher are added as auxiliary components, requiring no modification to SQLite's core execution or storage format. Compatibiliy: SQLite-LAP works transparently with both SQLite and LibSQL (including its VectorDB extension) without changing existing query semantics or on-disk layout. Docker A Dockerfile at the repo root builds both engines (sqlite3-construct and sqlite3-lap) into one image, with liburing built from source since it isn't packaged for the base image. docker build -t sqlite-lap .   Run it with a directory mounted at /data for your database files. SQLite-LAP's IORING_SETUP_SQPOLL mode needs extra permissions that Docker doesn't grant by default: docker run --rm -it \ --security-opt seccomp=unconfined \ --ulimit memlock=-1:-1 \ --cap-add SYS_ADMIN \ -v "$(pwd)/data:/data" \ sqlite-lap   Inside the container, sqlite3-construct/sqlite3-lap just open an interactive shell — you still type the SQL yourself (see the Example section below): sqlite3-construct test.db # build + .finconstruct sqlite3-lap test.db # read-only experiments   To automatically run that whole example end-to-end and verify it worked, run smoke-test instead (as the container command, or from inside the shell): docker run --rm \ --security-opt seccomp=unconfined \ --ulimit memlock=-1:-1 \ --cap-add SYS_ADMIN \ -v "$(pwd)/data:/data" \ sqlite-lap smoke-test   It builds a 5000-row table with init_construct_fin_table_src, runs .finconstruct, switches to SQLite-LAP, re-reads the table, and reports pass/fail plus the io_uring cache hit/miss stats. Optional args: smoke-test [db_path] [row_count]. Repository layout git clone https://github.com/korea-dbs/sqlite-lap.git cd sqlite-lap   Each engine variant is a self-contained SQLite 3.42.0 source tree: <variant>/ src/ # SQLite source (btree.c, shell.c.in, ...) ext/, tool/ # extensions + build tooling (fts5, rtree, lemon, mksqlite3c.tcl, ...) configure, Makefile.in, ...   SQLite-LAP and init_construct_fin_table_src are set up this way and build standalone — no external source tree is required. Prerequisites gcc, make, tclsh (used to generate the amalgamation during build) liburing dev headers (liburing.h, -luring) — required for SQLite-LAP only readline/ncurses dev headers (used by the shell CLI) Linux kernel with io_uring support (5.1+) for SQLite-LAP The two-engine workflow (important) There are two separate binaries, used for two separate purposes. Do not mix them up. Engine Use for Do NOT use for init_construct_fin_table_src Creating tables, loading/inserting data, running .finconstruct — SQLite-LAP Read-only queries (SELECT) on an already-constructed DB Creating tables or inserting data Always build and load your dataset with init_construct_fin_table_src first, run .finconstruct, and only then switch to the SQLite-LAP binary to run the read-side experiment. Engine variants ( libSQL-LAP | ReadAheadsrc | SQLite-LAP | Vanilla+BG | Vanilla+uring ) SQLite-LAP combines three major components: iouring, background execution, and FIN aware prefetching. Vanilla+ReadAhead enables readahead, which prefetches five consecutive pages following the currently accessed page in a single I/O operation, where five is selected as the number that yields the best performance in our empirical evaluation. Vanilla+uring issues batch read requests for the leaf pages under a FIN via iouring on the main thread upon reaching a FIN during B-tree traversal, but blocks the main thread until all I/O completions are acknowledged. Vanilla+BG decouples leaf-page I/O from the main thread via two background threads, the same thread count as SQLite-LAP, upon reaching a FIN during B-tree traversal, but issues a separate system call for each page read without batching. Build Build each engine the same way: cd SQLite-LAP # or init_construct_fin_table_src mkdir bld && cd bld ../configure   For SQLite-LAP only, edit bld/Makefile to link pthread/io_uring: CC = gcc CFLAGS = -g -O2 -DSQLITE_OS_UNIX=1 -pthread LIBS += -luring -pthread   (init_construct_fin_table_src does not use io_uring/threads, so it needs no Makefile edit.) make -j   This produces bld/sqlite3. .finconstruct Both engines' shells have a .finconstruct dot-command that builds/refreshes the FIN bitmap in one step — it replaces the old manual "create bitmap_table, then SELECT * every table by hand" process: .finconstruct   It creates bitmap_table if missing, then runs a full SELECT * scan over every user table. Each scan drives the FIN-promotion logic in btree.c's moveToChild(): qualifying interior pages get flagged as FIN (page-type byte 0x05 → 0x07) and the corresponding (fippgno, childpg) edges get recorded in bitmap_table. Run it once after loading data (before switching to SQLite-LAP), and again any time you add tables/rows through init_construct_fin_table_src. bitmap_table schema: CREATE TABLE bitmap_table ( fippgno INTEGER, childpg INTEGER, PRIMARY KEY (fippgno, childpg) );   Example: build a dataset and run it through SQLite-LAP # 1) Build the dataset with the construction-only engine ./init_construct_fin_table_src/bld/sqlite3 test.db   CREATE TABLE t1(a INTEGER PRIMARY KEY, b TEXT); WITH RECURSIVE seq(x) AS ( SELECT 1 UNION ALL SELECT x+1 FROM seq WHERE x < 5000 ) INSERT INTO t1 SELECT x, hex(randomblob(50)) FROM seq; .finconstruct SELECT count(*) FROM bitmap_table; -- sanity check: should be > 0 .quit   # 2) Switch to SQLite-LAP for the actual (read-only) experiment ./SQLite-LAP/bld/sqlite3 test.db   SELECT count(*), sum(length(b)) FROM t1; .quit   On open, SQLite-LAP prints ring init IAM-nomem / THD-Pool init to stderr once the io_uring ring and background thread pool are initialized. On .quit, it prints close func hit : N ,miss : M — the pager cache hit/miss count for the session. A high hit ratio here means the background prefetcher is successfully warming leaf pages (via bitmap_table + batched io_uring reads) ahead of the cursor reaching them.

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse.  It was decided to include only raw, unprocessed audio da…

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse.  It was decided to include only raw, unprocessed audio data files in each site-specific Zenodo record. This decision was so that any researcher can independently verify, reproduce, and extend the analysis performed. As a result, some sites have WAV files with 0 bytes of data or timestamps outside the range of probable recording times. Procedures used by the Eclipse Soundscapes team to process audio data for its purposes are outlined in the Data Management reports located in the Eclipse Soundscapes Zenodo community. Data with 0 bytes of data were included for completeness. When possible, all site-specific files, including the audio files, are included in a zip file for ease of download. If a zip file upload was not possible due to upload or bandwidth limitations, all available audio files are included individually. Data Site location information: Latitude: 38.640373  Longitude:  -89.097176 Local Eclipse Type: Total Solar Eclipse Eclipse Percent (%): 100 WAV files Time & Date Settings: Set with Automated AudioMoth Time Chime (More information on TimeStamp Setting below) Data Collector Start Time Notes: N/A Included Data: Audio files in WAV format with the date and time in UTC within the file name: YYYYMMDD_HHMMSS meaning YearMonthDay_HourMinuteSecondFor example, 20240411_141600.WAV means that this audio file starts on April 11, 2024 at 14:16:00 Coordinated Universal Time (UTC) CONFIG Text file: Includes AudioMoth device setting information, such as sample rate in Hertz (Hz), gain, firmware, etc.  README.md:  Markdown formatted file with information about the recording and recording site. file_list.csv:  A machine and human file that gives the following information on each file in the record: File Name, File Type, Description, File Size in kilobytes, Name of Associated Data Dictionary with the file, calculated SHA-512 Hash of the file as a unique identifier to insure data integrity during transfer and compression. total_eclipse_data.csv:  A machine and human readable file that gives the following information about the site where the audio data recording was taken: ESID#, Latitude, Longitude, Eclipse_type, CoveragePercent, Eclipse Start UTC (1st contact), Totality Start UTC (2nd contact), Totality End UTC (3rd Contact), Eclipse End UTC (4th Contact), Max Eclipse Time UTC License.txt:  A human readable file that explains the terms and conditions under which the data can be used. AudioMoth_Operation_Manual.pdf:  A human readable document that explains the use of an AudioMoth device. The document is current up to the time of the AudioMoth's use in the Eclipse Soundscapes project. file_list_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the file_list.csv file. CONFIG_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the CONFIG.TXT file. eclipse_data_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the total_eclipse_data.csv file. WAV_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the *.WAV files. ES_Data_Management_Pre-Eclipse_Data_Infrastructure_Stage_0.pdf:  PDF document that describes Stage 0 (Pre-Eclipse Infrastructure and Data Stewardship Planning) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Receipt_Sorting_and_Metadata_Organization_Stage_1.pdf:  PDF document that describes Stage 1 (Receipt, Sorting, and Metadata Organization) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Data_Processing_Stage_2.pdf:  PDF document that describes Stage 2 (Data Processing) of the Eclipse Soundscapes (ES) data Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio datalifecycle. ES_Data_Management_Data_Sharing_Stage_3.pdf:  PDF document that describes Stage 3 (Public Data Sharing) of the Eclipse Soundscapes (ES) data lifecycle. Eclipse Information for this location: Eclipse Date: April 08, 2024 Eclipse Start Time (UTC) (1st Contact): 17:44:29 Totality Start Time (UTC) (2nd Contact): [N/A if partial eclipse] 19:01:18 Eclipse Maximum Time [when the most possible amount of the Sun in blocked] (UTC): 19:02:24 Totality End Time (UTC) (3rd Contact): [N/A if partial eclipse] 19:03:30 Eclipse End Time (UTC) (4th Contact): [N/A if partial eclipse] 20:18:46 Audio Data Collection During Eclipse Week ES Data Collectors used AudioMoth devices to record audio data, known as soundscapes, over a 5-day period during the eclipse week: 2 days before the eclipse, the day of the eclipse, and 2 days after. The complete raw audio data collected by the Data Collector at the location mentioned above is provided here. This data may or may not cover the entire requested timeframe due to factors such as availability, technical issues, or other unforeseen circumstances.  ES ID# Information: Each AudioMoth recording device was assigned a unique Eclipse Soundscapes Identification Number (ES ID#). This identifier connects the audio data, submitted via a MicroSD card, with the latitude and longitude information provided by the data collector through an online form. The ES team used the ES ID# to link the audio data with its corresponding location information and then uploaded this raw audio data and location details to Zenodo. This process ensures the anonymity of the ES Data Collectors while allowing them to easily search for and access their audio data on Zenodo.  TimeStamp Information: The ES team and the Data Collectors took care to set the date and time on the AudioMoth recording devices using an AudioMoth time chime before deployment, ensuring that the recordings would have an automatic timestamp. However, participants also manually noted the date and start time as a backup in case the time chime setup failed. The notes above indicate whether the WAV audio files for this site were timestamped manually or with the automated AudioMoth time chime.   Common Timestamp Error: Some AudioMoth devices experienced a malfunction where the timestamp on audio files reverted to a date in 1970 or before, even after initially recording correctly. Despite this issue, the affected data was still included in this ES site's collected raw audio dataset. Latitude & Longitude Information: The latitude and longitude for each site was taken manually by data collectors and submitted to the ES team, either via a web form or on paper. It is shared in Decimal Degrees format. General Project Information: The Eclipse Soundscapes Project is a NASA Volunteer Science project funded by NASA Science Activation that is studying how eclipses affect life on Earth during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. Eclipse Soundscapes revisits an eclipse study from almost 100 years ago that showed that animals and insects are affected by solar eclipses! Like this study from 100 years ago, ES asked for the public's help. ES uses modern technology to continue to study how solar eclipses affect life on Earth!  Eclipse Soundscapes is an enterprise of ARISA Lab, LLC and is supported by NASA award No. 80NSSC21M0008. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration.   Eclipse map/figure/table/predictions courtesy of Fred Espenak, NASA/Goddard Space Flight Center, from eclipse.gsfc.nasa.gov.   Eclipse Data Version Definitions {1st digit = year, 2nd digit = Eclipse type (1=Total Solar Eclipse, 9=Annular Solar Eclipse, 0=Partial Solar Eclipse), 3rd digit is unused and in place for future use} 2023.9.0 = Week of October 14, 2023 Annular Eclipse Audio Data, Path of Annularity (Annular Eclipse) 2023.0.0 = Week of October 14, 2023 Annular Eclipse Audio Data, OFF the Path of Annularity (Partial Eclipse) 2024.1.0 = Week of April 8, 2024 Total Solar Eclipse Audio Data, Path of Totality (Total Solar Eclipse) 2024.0.0 =  Week of April 8, 2024 Total Solar Eclipse Audio Data , OFF the Path of Totality (Partial Solar Eclipse) *Please note that this dataset's version number is listed below. Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024) (Archival Copy) This site-level record includes the Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024). The manual documents the participant training, device setup procedures, metadata submission requirements, ES ID system, timestamp protocols, data return workflow, and public archiving processes used during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. The manual is preserved for transparency and reproducibility and reflects the procedures under which this dataset was collected and processed. (DOI 10.5281/zenodo.18623442) Data Receipt, Processing, and Analysis Methods All programs supporting Stages 1–3 are openly available in the: Eclipse Soundscapes GitHub repository: https://github.com/ARISA-Lab-LLC/ESCSP Data Management Lifecycle The following section documents the relationship of this record to the full Eclipse Soundscapes (ES) data lifecycle, a multi-stage workflow designed to support large-scale participatory science, long-term data stewardship, open science, and scientific reuse. Each stage addressed a different operational need, beginning before eclipse deployment and continuing through validation, public archiving, and scientific analysis. Together, these stages transformed distributed volunteer-submitted audio recordings into structured, documented, publicly accessible NASA-funded research assets. Stage 0: Pre-Eclipse Infrastructure and Deployment Preparation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Pre-Eclipse Infrastructure and Deployment Preparation (Stage 0). Zenodo. https://doi.org/10.5281/zenodo.20413370 Stage 0 focused on building the operational foundation required to support geographically distributed eclipse data collection at national scale. This stage included AudioMoth device preparation, accessibility modifications, ES ID # assignment systems, metadata collection workflows, participant training materials, deployment logistics, and planning for downstream data stewardship and archival workflows. The 2023 annular eclipse served as both a scientific investigation and a large-scale operational beta test that informed improvements for the 2024 total solar eclipse campaign. Related Citations and Resources: Severino, M., & Kline, T. (2025, November 24). Eclipse Soundscapes Apprentice Role Curriculum: Solar Eclipses and Multi-Sensory Observing (Informal Education). Zenodo. https://doi.org/10.5281/zenodo.17703003 Severino, M., & Bauer, D. J. (2026). Eclipse Soundscapes Observer Role Training and Resources Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18633602</li> Severino, M., Winter, H., & Bauer, D. J. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Stage 1: Receipt, Sorting, and Metadata Organization Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Receipt, Sorting, and Metadata Organization (Stage 1). Zenodo. https://doi.org/10.5281/zenodo.19471425 Stage 1 transformed returned participant materials into organized, traceable site-level records. This included receiving mailed microSD cards, consolidating participant-submitted metadata, reconciling handwritten and online records, organizing physical audio media by ES ID #, and deriving eclipse timing and coverage information using NASA eclipse prediction datasets. The outputs of Stage 1 established the structured metadata relationships required for downstream validation, processing, archiving, and analysis workflows. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). EPTT (Eclipse Phase Timing Tool) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-Eclipse-Phase-Timing-Tool/li> Espenak, F. (n.d.). Eclipse predictions by Fred Espenak, NASA's GSFC Eclipse Web Site. NASA Goddard Space Flight Center. http://eclipse.gsfc.nasa.gov/eclipse.html Stage 2: Data Processing and Validation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Data Processing (Stage 2). Zenodo. https://doi.org/10.5281/zenodo.18683402 Stage 2 focused on centralized audio ingestion, validation, timestamp verification, metadata reconciliation, and preparation of datasets for analysis and public sharing. During this stage, returned audio recordings were processed using custom open-source tools developed by the ES team, including ES WAVES and ES AMES. The project implemented scalable infrastructure capable of processing large volumes of participant-submitted microSD cards while preserving all raw audio data without modification. Stage 2 established the validated dataset structure required for long-term preservation and scientific analysis. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). ES WAVES (Eclipse Soundscapes WAV Audio Validation & Extraction Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-WAV-Audio-Validation-Extraction-Suite Winter, H., & Goncalves, J. (2026). ES AMES (Eclipse Soundscapes AudioMoth Metadata Extractor Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-AMES-AudioMoth-Metadata-Extractor-Suite Stage 3: Public Data Sharing and Open Archiving Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Public Audio Data Sharing (Stage 3). Zenodo.https://doi.org/10.5281/zenodo.18683437 Stage 3 transformed validated site-level datasets into publicly archived, DOI-assigned research records published through the Eclipse Soundscapes Zenodo Community. This stage included dataset packaging, metadata standardization, README generation, integrity verification, DOI assignment, and automated repository upload workflows using the Automated Zenodo Upload Software (AZUS). These workflows established the project's long-term open-science infrastructure and ensured that datasets remained findable, accessible, interoperable, reusable, and citable for future scientific and educational use. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). AZUS (Automated Zenodo Upload Software) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/AZUS-Automated-Zenodo-Upload-Software Stage 4: Scientific Analysis and Research Use Stage 4 involves the scientific analysis and interpretation of validated eclipse soundscape datasets. Analysis workflows utilized datasets verified during earlier stages to investigate eclipse-related environmental and animal vocalization changes across hundreds of recording sites. This stage also includes broader scientific interpretation, publication development, and continued reuse of Eclipse Soundscapes datasets and infrastructure for future research, education, and open-science applications. Related Citations and Resources: Pease, B., Gilbert, N., & Severino, M. (2026). Eclipse Soundscapes Preliminary Findings – How Eclipses Affect Nature as determined by Sound (Recorded Webinar). Zenodo.https://doi.org/10.5281/zenodo.18613979 Gilbert, N. A., Pease, B. S., Severino, M., & Winter, H. III. (2026). Photic niche explains avian behavioral responses to solar eclipses. Ecology and Evolution, 16(2), e73090. https://doi.org/10.1002/ece3.73090 Analysis code repository: https://github.com/BrentPease1/eclipse-traits Companion Zenodo record archiving structured analysis scripts and derived outputs: https://doi.org/10.5281/zenodo.15790879[r] Public Archiving, Privacy, and Data Transparency The Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023-2024) includes a detailed explanation of how Eclipse Soundscapes audio data are publicly archived on Zenodo, how participant privacy is protected through the ES ID system, and how transparency and traceability are maintained. It also outlines the criteria for determining which recordings are included in the public archive, as well as the distinction between publicly shared archival data and datasets used for ES-led scientific analyses. Participants and data users can consult this section for full documentation of the project's open science and privacy practices. Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Citations Individual Site Citation: APA Citation (7th edition) Winter, H., Severino, M., & Volunteer Scientist. (2026). 2024 solar eclipse soundscapes audio data [Audio dataset, ES ID# 845]. Zenodo.{Insert DOI} Collected by volunteer scientists as part of the Eclipse Soundscapes Project. This project is supported by NASA award No. 80NSSC21M0008. Eclipse Community Citation Winter, H., Severino, M., & Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio data [Collection of audio datasets]. Eclipse Soundscapes Community, Zenodo. https://zenodo.org/communities/eclipsesoundscapes/ Collected by volunteer scientists as part of the Eclipse Soundscapes Project This project is supported by NASA award No. 80NSSC21M0008.  

Sistema de selección del alumnado proyecto FIVAC para ACSA

Im Datensatz "7-Tage-Inzidenz der COVID-19-Fälle in Deutschland" des Robert Koch-Instituts werden die täglich aktualisierten COVID-19-Inzidenzen auf Bundes-, Landes- und Landkreisebene basierend auf d…

Im Datensatz "7-Tage-Inzidenz der COVID-19-Fälle in Deutschland" des Robert Koch-Instituts werden die täglich aktualisierten COVID-19-Inzidenzen auf Bundes-, Landes- und Landkreisebene basierend auf den Meldungen nach dem Infektionsschutzgesetz (IfSG) bereitgestellt. Die 7-Tage-Inzidenz wird definiert als Anzahl gemeldeter Fälle der letzten sieben Tage pro 100.000 Einwohner:innen und differenziert nach Altersgruppen und Regionen. Die Datenverarbeitung erfolgt vollständig elektronisch über das Deutsche Elektronische Melde- und Informationssystem (DEMIS).

Im Datensatz "COVID-19-Todesfälle in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu COVID-19-bedingten Todesfällen in Deutschland bereitgestellt. Die Datenerhebun…

Im Datensatz "COVID-19-Todesfälle in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu COVID-19-bedingten Todesfällen in Deutschland bereitgestellt. Die Datenerhebung erfolgt im Rahmen des Infektionsschutzgesetzes (IfSG). Der Datensatz umfasst aggregierte Todesfallzahlen nach Berichtsdatum, Altersgruppen und Bundesländern und wird ergänzt durch den Fall-Verstorbenen-Anteil. Der Datensatz ermöglicht fundierte Analysen zur Bewertung der Krankheitsschwere und Verbreitung von COVID-19 in Deutschland.

Im Datensatz "COVID-19-Hospitalisierungen in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu Hospitalisierungen im Zusammenhang mit COVID-19 basierend auf den Meld…

Im Datensatz "COVID-19-Hospitalisierungen in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu Hospitalisierungen im Zusammenhang mit COVID-19 basierend auf den Meldungen nach dem Infektionsschutzgesetz (IfSG) bereitgestellt. Neben tagesaktuellen Fallzahlen und Inzidenzen auf Bundes- und Länderebene enthält der Datensatz auch modellgestützte Schätzungen verzögert gemeldeter Hospitalisierungen mittels Nowcasting. Ziel ist es, die tatsächliche Belastung des Gesundheitssystems durch COVID-19 besser abzubilden, insbesondere durch die Berechnung adjustierter 7-Tage-Hospitalisierungsinzidenzen.

Umbrella-sampling MD and mass-transfer regime-map code and data testing whether interfacial desolvation rate-limits small-molecule adsorption on pristine graphene from water.