Since 2018, the Specialised Information Service for Mobility and Transport Research (FID move) has been established by the Saxon State and University Library Dresden (SLUB) and the TIB – Leibniz Information Centre for Science and Technology as part of the DFG funding program "Specialised Information Services for Science". The FID move aims to develop and establish services and online tools in close consultation with the transport and mobility science community, that support this community in the entire research cycle. Research data are the fuel of scientific progress, and especially in mobility and transport research, there would be no progress without them. This makes it all the more important to increase the availability, findability, and accessibility of reusable research data. To this end, the FID move has developed a Research Data Repository based on the open-source software CKAN, which provides a simple and low-barrier opportunity for data publication according to the FAIR Data Principles. Do you have any questions about the Research Data Repository, data publication and curation, or research data management in general? Then please feel free to contact us by phone or at the email address below. We will be happy to help you. Provider of the Repository: Technische Informationsbibliothek (TIB) Welfengarten 1 B 30167 Hannover Germany Contact: Mathias Begoin Tel.: 0511 762-14140 E-Mail: forschungsdaten@fid-move.de Translated from German Original Text: Der Fachinformationsdienst Mobilitäts- und Verkehrsforschung (FID move) wird seit 2018 von der Sächsischen Landesbibliothek – Staats und Universitätsbibliothek Dresden (SLUB) und der Technischen Informationsbibliothek Hannover (TIB) im Rahmen des DFG-Förderprogramms "Fachinformationsdienste für die Wissenschaft" aufgebaut. Ziel des FID move ist es, in enger Abstimmung mit der verkehrs- und mobilitätswissenschaftlichen Fachcommunity, Dienstleistungen und Online-Werkzeuge zu entwickeln und aufzubauen, die diese im gesamten Forschungskreislauf unterstützen. Forschungsdaten sind der Treibstoff des wissenschaftlichen Fortschritts und insbesondere in der Mobilitäts- und Verkehrsforschung würde es ohne sie nicht vorwärts gehen. Umso wichtiger ist es, die Verfügbarkeit, Auffindbarkeit und Zugänglichkeit nachnutzbarer Forschungsdaten zu erhöhen. Hierzu wurde im FID move ein Forschungsdatenrepositorium auf Basis der Open-Source-Software CKAN entwickelt, welches eine einfache und niederschwellige Möglichkeit zur Datenpublikation nach FAIR-Data-Prinzipien ermöglicht. Haben Sie Fragen zum Forschungsdatenrepositorium, zu Datenpublikation und -kuratierung oder zum Forschungsdatenmanagement allgemein? Dann kontaktieren Sie uns gerne telefonisch oder unter der unten angegebenen E-Mail-Adresse. Wir helfen Ihnen gerne weiter. Anbieter des Repositoriums: Technische Informationsbibliothek (TIB) Welfengarten 1 B 30167 Hannover Deutschland Ansprechpartner und Kontakt: Mathias Begoin Tel.: 0511 762-14140 E-Mail: forschungsdaten@fid-move.de

Processed data connected with A moving target: trade-offs between maximizing carbon and minimizing hydraulic stress for plants in a changing climate

This dataset provides reproduction code and experimental data for the publication "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects". The repository contains an exact snapshot of the code version used to generate all results in the paper, ensuring full reproducibility. The repository is organized into three zip archives: code.zip: Contains the code used to generate and evaluate the data. This archive includes a README with instructions on how to use the code and integrate the two data archives. data_quantum_devices.zip:Contains the raw and partially preprocessed experimental data obtained from quantum devices, as well as calibration data for the devices. data_simulations.zip: Contains experimental data generated from simulations. For reference, this dataset includes a separate PDF file for each figure presented in the publication. These files were generated directly from the enclosed code and data, and they serve as benchmarks for visually verifying reproduced results.

These are the outputs of the CoreTrustSeal Revision Working Group and describe the CoreTrustSeal Requirements 2023-2025 and a change log of the revisions made with respect to the CoreTrustSeal Requirements 2020-2022. The CoreTrustSeal Requirements describe the characteristics required to be a trustworthy repository for digital data and metadata. Each Requirement is accompanied by Guidance text describing the response statements and evidence that applicants must provide to enable an objective review. Applicants must respond to all of the Requirements.

The National Heart, Lung, and Blood Institute (NHLBI), within the United States’ National Institutes of Health (NIH), established the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) in 2008 to develop the infrastructure needed to link the contents of the NHLBI Biorepository and the NHLBI Data Repository, and to promote the utilization of these scientific resources by the broader research community. Program utilization metrics were developed to measure the impact of BioLINCC on Biorepository access by researchers, including visibility, program efficiency, user characteristics, scientific impact, and research types. Input data elements were defined and are continually populated as requests move through the process of initiation through fulfillment and publication. This paper reviews the elements of the tracking metrics which were developed for BioLINCC and reports the results for the first six on-line years of the program.

This repository contains a cabled, indoor (office environment) and an outdoor (suburban) DASH7 data set. All data sets are formatted as sigmf-data and sigmf-meta files which can be investigated with IQEngine, GNU Radio or MATLAB. The original samples were recorded as complex float 32-bit samples and have been converted to complex signed int 16-bit samples. Below you can find a more extended description of the data sets.

Requestor characteristics by website year.

Dataset provided for publication in Frontiers in Forests and Global Change : "Climate change increases the severity and duration of soil water stress in the temperate forest of eastern North America".

Aim: Many rare species are dispersal-limited and minimal land use and climate changes can impact their colonization capacity. Most ecological niche models predict the distribution of species under future climate and land use change scenarios without incorporating specie-specific dispersal abilities. Here we investigated the effect of climate and land use change on low vagile species accounting for their dispersal capacity and defined accessible areas in the future.

Location: Europe.

Taxon: Saproxylic beetles.

Methods: We used the current (2007-2012) occurrences of six endangered saproxylics to develop ecological niche models using current climate and land use conditions. We projected species distributions under four future climate and land use change scenarios to estimate their potential occurrences. Finally, accounting for species-specific dispersal, we limited their distributions to accessible areas in 2040-50.

Results: Without accounting for dispersal abilities we found a strong ...

SWAP efficiency tau_alpha^std/tau_alpha^swap as a function of the relaxation time for the standard dynamics (representing the sluggishness) for different polydispersities in d=3, 4, 5, and 6.

Datasets generated during and after Jabari Jones' Master's thesis at Utah State University, focused on channel change of Sixth Water Creek and Diamond Fork River, Utah, USA (Jones, J.C., 2018. Historical channel change caused by a century of flow alteration on Sixth Water Creek and Diamond Fork River, UT. Master's thesis, Utah State University). This resource includes data collected in the field as well as data generated in GIS. Field data include cross-section surveys, RTK GPS surveys, sediment transport measurements, bed grain size analysis, and unmanned aerial vehicle (drone) photography. GIS data include shapefiles generated from aerial imagery, digital elevation models, and data generated to evaluate incision of the Sixth Water valley. Data were collected and generated between July 2016 and November 2021 All data, metadata and related materials meet the quality standards relative to the purpose for which they were collected and generated.

Data added to this updated resource include channel width measurements from 2018 aerial photographs, regional width analysis from 2018 aerial photographs, and an analysis of incision in the Sixth Water and Upper Diamond Fork valleys.

This dataset tracks the updates made on the dataset "Upcoming Reporting Cadence Change" as a repository for previous versions of the data and metadata.

SWAP efficiency tau_alpha^std/tau_alpha^swap as a function of the degree of polydispersity at different values of tau_alpha^std.

This dataset tracks the updates made on the dataset "Skilled Nursing Facility Change of Ownership" as a repository for previous versions of the data and metadata.

Reasons biospecimen requests were not fulfilled, by website year.

Change in Retail Trade: Miscellaneous Store Retailers Payroll Employment in Texas was -0.74982 Thous. of Persons in July of 2022, according to the United States Federal Reserve. Historically, Change in Retail Trade: Miscellaneous Store Retailers Payroll Employment in Texas reached a record high of 5.45718 in May of 2020 and a record low of -18.68742 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Change in Retail Trade: Miscellaneous Store Retailers Payroll Employment in Texas - last updated from the United States Federal Reserve on June of 2025.

Structural relaxation time tau_alpha for the standard and the SWAP dynamics for various particle size distributions P(sigma) with delta = 10% in d = 4.

This dataset represents the data extracted as part of a CGM-based biological feedback scoping review.

Reproducibility data for a quantitative study on EU legislation

The files in this repository were generated or used in a pipeline of analysis operations on EU legislation published between 1971 and 2022. The project is called the Nature of EU Rules which seeks to analyse the "strictness" and density of EU regulations over time and by legal policy area. The data has been made available to help make the results of our study reproducible by other researchers. The underlying data used in the study has also been published in this repository.

File descriptions

complete_training_data.csv

This file is training data for binary classification of specific sentences in EU legislation as either regulatory in nature (constituting a legal obligation for some agent) or not (called a constitutive statement). The sentences have been labelled by EU law professors from Aarhus University in Denmark and Radboud University in the Netherlands

Note: The file also contains columns for identifying the specific agent being regulated (to which the legal obligation applies) in each sentence. However, this information has not been used in the study

extracted_sentences_classified_1971_2022.csv

List of sentences extracted from EU legislation documents

Classification results for individual sentences whether each is regulatory or not. There are two columns recording the classification results, one for a rule-based approach (using grammatical dependency parsing) and one for a LegalBERT classification approach.

inlegal_bert_xgboost_classifier.json

Trained binary classification model for classifying sentences as regulatory or not (based on InlegalBERT).

Note: this model is trained on the file 'complete_training_data.csv' in this Zenodo repo

Model was trained using this script and used by these scripts: one, two

metadata_enriched.csv

Metadata file from this repository but enriched with additional columns one of which is the count of regulatory sentences in each individual document

File is generated by this script

File is used by this script

classification_results_all_algorithms_test_set.csv

classification results of each sentence in the test set containing 1451 sentences (20% of training set)

according to both the fine-tuned Legal-BERT model and the dependency parsing (rule-based) algorithm

also contains the ground truth labels

Github repositories relevant to this analysis

The Python scripts in the following Github repositories were responsible for generating the data files in this Zenodo repository. The first repository listed is the core one for running the pipeline to classify and quantitatively analyse legal obligations in EU legislation. The other listed Github repositories represent components or steps of the pipeline.

http://github.com/nature-of-eu-rules/eu-legislation-strictness-analysis

http://github.com/nature-of-eu-rules/data-extraction

http://github.com/nature-of-eu-rules/data-preprocessing

http://github.com/nature-of-eu-rules/regulatory-statement-classification

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.