cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2dqsjg
title: CCDC 2226436: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Santiago de la Moya|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dqw12
title: CCDC 2226514: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Kalle Nättinen, Roland Fröhlich, Oleg Lukin, Fritz Vögtle|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dr1dm
title: CCDC 2226680: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Holger Schwierz |2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dr26g
title: CCDC 2226705: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Wolfram Mennicke|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dr7w9
title: CCDC 2226881: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Marcus Plevoets|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dx6q8
title: CCDC 2231650: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Andreas König|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dx78v
title: CCDC 2231667: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Stefan Braschohs|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dx7xh
title: CCDC 2231687: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Stefan Braschohs|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.22000/938
title: ConteJAS2023

description: Data to reproduce the figures in publication.
DOI: 10.6084/m9.figshare.24573130
title: Annex. TV Series Reference List for the academic article "Old Wine in New Bottles. Narrative complexity in the dawn of Netflix Originals (2013–2017)"

description: This is the annex containing the TV titles of the Netflix Originals cited in an academic article.
DOI: 10.6084/m9.figshare.24573130.v2
title: Annex. TV Series Reference List for the academic article "Old Wine in New Bottles. Narrative complexity in the dawn of Netflix Originals (2013–2017)"

description: This is the annex containing the TV titles of the Netflix Originals cited in an academic article.
DOI: 10.6084/m9.figshare.24573130.v1
title: Annex. TV Series Reference List for the academic article "Old Wine in New Bottles. Narrative complexity in the dawn of Netflix Originals (2013–2017)"

description: This is the annex containing the TV titles of the Netflix Originals cited in an academic article.
DOI: 10.4121/2796f502-0610-4a7d-a8ee-ebc36639e0b1
title: Data and analysis code for the Masters thesis titled "Dyadic Physical Activity Planning with a Virtual Coach: Using Reinforcement Learning to Select Persuasive Strategies"

description: This is the data and analysis code for the Masters thesis titled "Dyadic Physical Activity Planning with a Virtual Coach: Using Reinforcement Learning to Select Persuasive Strategies." In this thesis, we used data gathered through an observational study with a virtual coach to build a reinforcement learning model for persuading people to commit to plans for walking. Once the model was created, it was analysed using the code provided in this dataset, including simulations of how the model would perform in a real-life setting.<br><strong>Study</strong>The study was conducted on the online crowdsourcing platform Prolific in June and July 2023. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 3089). 114 participants participated in a conversation with the virtual coach named Jamie. During these conversations, the virtual coach measured people's confidence in following the plan, their perceived usefulness of planning, and their attitude towards planning. Then, the virtual coach used a persuasive strategy to try to change these aspects of a person's state, after which it measured the three aspects again. This repeated until at least two persuasive strategies were used, and until the three aspects were high enough to satisfy pre-set heuristics for when the person is likely to commit to the plan. When these conditions were met, the virtual coach measured the reward signal, composed of satisfaction with the conversation, commitment to the fist two weeks and to the whole plan, and confidence in reaching the goal.<br><strong>Data</strong>We provide data on:<br>participant characteristics (e.g., age, gender, stage of change for becoming physically active),the state-action-next state-reward samples used to create the reinforcement learning model,free-text responses to the persuasive strategies used by the virtual coach,free-text responses about what people found motivating and demotivating in motivational messages.<br>The file OSF pre-registration explains in detail how each variable was measured.<br><strong>Analysis</strong>We provide code to reproduce our analysis with Docker. For more information on this, please refer to the README-file in this repository.<br>In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).
DOI: 10.3886/e193861v1
title: Further Evidence on Inflation Targeting and Income Distribution

description: This study examines the effect of inflation targeting (IT) on income distribution in a panel of 70 countries. Employing a variety of propensity score matching methods, we find strong evidence that that incomes became more unequal in IT-adopting countries relative to countries that did not adopt IT. On average, IT has been associated with a relative rise in the pre- and post-tax Gini coefficients of about 2 percentage points, and a relative increase in the share of national income going to the top 1% and 10% of households by about 12 percentage points and 13-17 percentage points.
description: Household income.
description: coded on-site observation; Annual data collected from publicly available databases, mainly the World Bank and the International Monetary Fund
DOI: 10.5281/zenodo.10136622
title: EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency

description: This dataset contains trained deep learning models, dataset and experiment files for the manuscript "EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency". Please find the software and more information including tutorials here: MMV-Lab/EfficientBioAI (github.com).
DOI: 10.5281/zenodo.10136623
title: EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency

description: This dataset contains trained deep learning models, dataset and experiment files for the manuscript "EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency". Please find the software and more information including tutorials here: MMV-Lab/EfficientBioAI (github.com).
DOI: 10.25835/lm02jmx0
title: Data for paper: "Method Comparison for Simulating Non-Gaussian Beams and Diffraction for Precision Interferometry"

description: ## 1. Introduction Welcome to the documentation for the code, data, and plotting scripts corresponding to the paper titled ["Method Comparison for Simulating Non-Gaussian Beams and Diffraction for Precision Interferometry"](https://doi.org/10.3390/s23229024). This repository contains resources to replicate the simulation results presented in the paper. Please carefully review the instructions below to ensure the successful execution of the code, data generation, and plotting. ## 2. Dependencies Before running the code, it is necessary to install IfoCAD (version 2022/10, git commit adf19a5b) in your environment. Please note, that IfoCAD is currently prepared for publication. However, because IfoCAD is not yet publically available, we provide here likewise the data generated by IfoCAD. Here is the introduction page of IfoCAD: https://www.aei.mpg.de/ifocad. Additionally, the plotting scripts require the installation of gnuplot. ## 3. Directory Structure The code, data, and plotting scripts are organized according to the structure of the paper's sections. Each section corresponds to a specific folder. Here is a brief overview of the file structure: Section2/: Code, data, and plotting scripts for Section 2 Section3/: Code, data, and plotting scripts for Section 3 ... ## 4. Running the Code ### Step 1: Install Dependencies Ensure IfoCAD is installed. The open-source release is anticipated soon. Also, make sure gnuplot is installed for the plotting scripts. ### Step 2: Run the Code Navigate to the specific section folder and to run the code. ## 5. Generating Data After running the code, the generated data will be stored in the file path configured in the code. Utilize this data for further analysis and visualization. ## 6. Plotting Scripts Each section's plotting scripts are located in their respective folders. Run these scripts sequentially to generate plots consistent with those presented in the paper. Make sure to have gnuplot installed for proper execution. For any issues or questions during usage, feel free to contact us. Thank you for using our resources!
DOI: 10.5281/zenodo.10102665
title: Dataset: Thermal noise calibration of functionalized cantilevers for force microscopy: effects of the colloidal probe position.

description: Dataset for article "Thermal noise calibration of functionalized cantilevers for force microscopy: effects of the colloidal probe position"
The raw data is in the 6 zip files (AIO*.zip), which contains thermal noise spectra measured on the raw cantilevers close to the free end (folders AIO#, where #=1, 2 or 3 for samples A, B or C), or on the loaded cantilever at various positions along its length (folders AIO#-ScanHF, where #=1, 2 or 3 for samples A, B or C). The file format is Matlab data file (.mat), it include the vectors f (for frequency axis, in Hz) and p (power spectrum density, in m^2/Hz). Other variables are erreur (some internal check that the calibration of the interferometer is pertinent) and PointW (mean intensity collected by the interferometer, and mean contrast on the 2 quadrature signals). For the loaded cantilever, we also record the laser spot position (XFaisceau and YFaisceau, in µm, origin close to the free end on the cantilever), some timing information to track for drifts, and ellipse (a calibration step of the quadrature phase interferometer).
The SEM images of the samples are included in the zip file SEMimages.zip
All analysis scripts (Matlab .m files) are included:

AnalyseAll.m reads the raw data files and extract all the pertinent information, saving it to file AnalyseAll.mat
Analysis_cp analyses the pre-processed data with the single contact point model
Analysis_endload analyses the pre-processed data with the rigid end load model
figspectrum.m creates Fig. 4 of the article
plot_cp_endload.m creates Fig. 5 and 6 of the article
All other scripts (.m) are dependencies that are necessary for the 3 former scripts to run. All scripts are commented and should be self explanatory. Of interest are the scripts mode_shape_cp.m and mode_shape_endload.m, which compute the resonant mode shape of a loaded cantilever for the two models, when given the parameters on the load size and position.
DOI: 10.6084/m9.figshare.22844264
title: Reward prediction error facilitates agile motor adaptations

description: Data obtained during study described in the paper Explicit learning based on Reward prediction error facilitates agile motor adaptations.S1 - dataCPA. File containing Cursor Path Area data from all 24 subjectsThe data is composed of 24 rows and 300 columns, where the first 8 rows represent ERR group, next 8 rows represent EPE group and the last 8 rows represent RWD group. Each column shows Cursor Path Area for a specific trial.S2 dataAngle. File containing Cursor angle data from all 24 subjectsThe data is composed of 24 rows and 300 columns, where the first 8 rows represent ERR group, next 8 rows represent EPE group and the last 8 rows represent RWD group. Each column shows Cursor angle for a specific trial.
DOI: 10.6084/m9.figshare.22844264.v1
title: Reward prediction error facilitates agile motor adaptations

description: Data obtained during study described in the paper Reward prediction error facilitates agile motor adaptations. <br> S1 Data. File containing TA data from all 16 subjects The data is composed of 16 rows and 300 columns, where the first 8 rows represent ERR group and the last 8 rows represent RWD group. Each column shows TA for a specific trial. <br> S2 Data. File containing Reach angle data from all 16 subjects The data is composed of 16 rows and 300 columns, where the first 8 rows represent ERR group and the last 8 rows represent RWD group. Each column shows reach angle for a specific trial.
DOI: 10.5281/zenodo.10143441
title: Master File PG
DOI: 10.5281/zenodo.10143442
title: Master File PG
DOI: 10.6073/pasta/e63a877c079f4923f9cbfd2662ec2ff7
title: AGW01 Long-term measurement of groundwater physical and chemical properties from wells on watershed N04D at Konza Prairie

description: In 1988 and 1990, the U.S.Geological Survey, Lawrence, KS, drilled 31 wells at 20 sites within the N04D watershed at the Konza Prairie Research Natural Area. The wells range in depth from about 2 to 13 meters, and are nested to include wells completed in alluvium/colluvium near the N04D drainage and in two Permian-aged limestone&rsquo;s. In 1997, Konza LTER drilled an additional 4 wells at 3 sites, that range in depth from about 12 m to 37 m. The sites comprise four transects running approximately east-west across the drainage, and occupy the lower one-quarter of the surface area of the watershed. The geology of the area is characterized by patchy, near-stream alluvium/colluvium which overlies bedrock that is composed of thin (1-2 meter) limestones alternating with thicker (2-4 meter) shales. Beginning in 1990, water levels were measured and water samples collected quarterly. Since 1991, water levels have been measured and water samples for inorganic chemical analysis have been collected every four to six weeks; sample collection is by bailing wells after removing 1-2 well volumes of groundwater. The groundwater is then filtered through 0.45 micro; membrane filters by gravity feed or using a peristaltic pump. One 250-mL LDPE bottle is filled with 250 mL of sample and 5 mL of concentrated HNO3 for determination of major and minor cation concentrations. One 250-mL LDPE bottle is filled to the brim for anion determination by ion chromatography and alkalinity determination by titration with 0.02 N H2SO4. The bottles are stored on ice and later in a refrigerator.
DOI: 10.6073/pasta/7acd00fa71080ea385eaefb8a7dc124b
title: AWE01 Meteorological data from the konza prairie headquarters weather station

description: The following weather data are included in this data set: hourly (record type 1): mean temperature, mean relative humidity, mean wind speed, mean wind direction, mean solar radiation, soil temperature, max wind speed; daily (record type 2): maximum air temperature, minimum air temperature, mean air temperature, mean relative humidity, total solar radiation, total precipitation, maximum soil temperature, minimum soil temperature, mean soil temperature, average wind speed. These data are collectecd by a micrologger at headquarters on Konza Prairie.
DOI: 10.7910/dvn/kewp9p
title: Prueba Dadun_UNAV

description: Prueba Unav_Dadun
DOI: 10.13120/6621ec8c-a825-47cd-8cd8-4f7b990241be
title: European floating micro-litter standardized, harmonized and validated datasets 2011/2020 v2023
title: European_floating_micro-litter_standardized_harmonized_and_validated_datasets_2011/2020_v2023

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification, contaminants and litter. The chosen parameters are relevant for the Marine Strategy Framework Directive (MSFD), in particular for descriptors 5, 8, 9 and 10. These datasets contain standardized, harmonized and validated data collections of floating micro-litter. For floating micro-litter, the harmonized datasets contain all unrestricted EMODnet Chemistry data on floating micro-litter data. Data was harmonized by ‘Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)’ from Italy. Data is formatted following Guidelines and formats for gathering and management of micro-litter data sets on a European scale (floating and sediment micro-litter), which can be found at: https://doi.org/10.6092/d3e239ec-f790-4ee4-9bb4-c32ef39b426d. Parameter names in these datasets are based on P01, Parameter Usage Vocabulary, which is available at: https://vocab.seadatanet.org/p01-facet-search. Each measurement value has a quality flag indicator. The resulting data collections are harmonized, using ODV Software and following a common methodology for all Sea Regions. Harmonization means that: (1) unit conversion is carried out to express variables with a limited set of measurement units and (2) merging of variables described by different “local names”, but corresponding exactly to the same concepts in BODC P01 vocabulary. The harmonized dataset can be downloaded as ODV collection that can be opened with ODV software for visualization (More information can be found at: https://www.seadatanet.org/Software/ODV). The same dataset is offered as spreadsheet (txt format, tab separated values) where the values of the categories for the following reported parameters (type, shape, size, color, transparency and material) have been uniformed as labelled in the SeaDataNet H01, H02, H03, H04, H05, H06 vocabularies (https://vocab.seadatanet.org/search). This format is more adapted to worksheet applications (e.g. LibreOffice Calc).
DOI: 10.5519/qd.sd7dpkyc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (640 records)
DOI: 10.5281/zenodo.10143470
title: Data related to "Complementary Taxation of Carbon Emissions and Local Air Pollution"

description: This is the database necessary to run the underlying model codes in GAMS. See https://github.com/MathiasMier/CO2andAPtaxation for codes and description.
DOI: 10.5281/zenodo.10143471
title: Data related to "Complementary Taxation of Carbon Emissions and Local Air Pollution"

description: This is the database necessary to run the underlying model codes in GAMS. See https://github.com/MathiasMier/CO2andAPtaxation for codes and description.
DOI: 10.6084/m9.figshare.24573961.v1
title: refmap inputs for panpipes

description: dataset used to demonstrate refmap
DOI: 10.6084/m9.figshare.24470044.v3
title: Ocean Temperature Variability Data on the Surface Along the Portuguese Coast

description: Monitoring ocean surface temperature is critical in detecting potential climatic changes and characterizing ocean temperature behaviour. Understanding the climatological trends and anomalies of sea surface temperatures is essential to comprehend the long-term effects of climate change on marine ecosystems and coastal regions. The collected dataset contains data from seven buoys of Ondograph and Meteo-oceanography types anchored offshore and along the Portuguese coast to acquire ocean surface temperatures. The data gathered provides a considerable temporal window, with an average interval of 14 years per buoy, enabling the creation of data series and the implementation of data mining algorithms to develop decision support systems. Collecting data <i>in situ</i> makes it possible to validate simulated results obtained using approximation models. This allows for more accurate temperature readings and facilitates testing and correcting created models.
DOI: 10.5281/zenodo.10143351
title: Dataset of comprehensive Full-notch creep tests (FNCT) of selected high-density polyethylene (PE-HD) materials

description: The dataset provided in this repository comprises data obtained from a series of full-notch creep tests (FNCT) performed on selected high-density polyethylene (PE-HD) materials (for further details, see section 1 Materials in this document) in accordance with the corresponding standard ISO 16770 [1]. 
The FNCT is one of the mechanical testing procedures used to characterize polymer materials with respect to their environmental stress cracking (ESC) behavior. It is widely applied for PE-HD materials, that are predominantly used for pipe and container applications. It is based on the determination of the time to failure for a test specimen under constant mechanical load in a well-defined and temperature controlled liquid environment. The test device used here also allows for continuous monitoring of applied force, specimen elongation and temperature.