cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48577/jpl.sqzsfh
title: Data set for the article ‘Diurnal cycle of rapid air temperature fluctuations at Jezero crater: probability distributions, exponential tails, scaling, and intermittency.’

description: This dataset includes (1) Sol${soln}_filters.txt temperatures at the Air Temperature Sensors (ATS) 1, 2, 3 at the surface, wind speed at the wind sensor 1 (WS1), and background wind speed as a function of spacecraft clock seconds, sol, Local Mean Mars Time, sol in Local True Solar Time, Local True solar time in seconds at Jezero, as estimated from MEDA measurements. Additional columns include the 720-second and 900-second running means with gaussian weighting excluding outliers and including the full time intervals, for the ATS1, surface temperature, and the WS1; The differences between observation and 900-second running means. Last column includes the bulk Richardson number based on those running mean values. One file for each sol number ${soln} 0116, 0117, and 0118 since landing on Jezero. (2) Sol*_ATS1_Sine_series.txt Least squares fit of the daily temperature to a series of sines and a phase shift; (3) Fig1a_ATS*_difs.txt Block-averaged differences between each sensor, ATS1, ATS2, ATS3, and the coldest of the three as a function of Local True Solar Time LTST.; (4) ATS145cm_std.txt Block averaged standard deviations of the differences between one of the two colder ATS, within a 1.5K difference range, and its fit to a series of 12 sine functions; (5) Figures9.dat Data used to plot figures 9. (6) README Includes a description of the file contents.
DOI: 10.6084/m9.figshare.24053247
title: Mare Aging metabolomics raw data.xlsx

description: Metabolic profile of oocytes, cumulus cells, and granuloma cells from old or young mares collected at 3 different maturation time points (0h, 24h, 42h form induction of follicular maturation). GCMS and LCMS datasets.
DOI: 10.6084/m9.figshare.24053247.v1
title: Mare Aging metabolomics raw data.xlsx

description: Metabolic profile of oocytes, cumulus cells, and granuloma cells from old or young mares collected at 3 different maturation time points (0h, 24h, 42h form induction of follicular maturation). GCMS and LCMS datasets.
DOI: 10.6084/m9.figshare.23576616
title: [DATASET] How do rift-related fault network distributions evolve?

description: Here we provide the Supplementary Material for the publication <b>'</b><b>How do rift-related fault network distributions evolve? Quantitative comparisons between natural fault observations and 3D numerical models of continental extension' </b>by <i>Pan, Naliboff, Bell and Jackson</i> in <i>Tectonics</i>. (1) <b>Models:</b> these contain the five models (A-E) as an interpolated numpy grid. The grid is a 2D array given that we extract a 2D depth slice 5 km beneath the initial model grid. The original ASPECT parameter file is also included for each model.<br><br>(2) <b>Scripts:</b><b> </b>contains the python scripts to generate fault statistics. All scripts use the interpolated numpy grid as a model input. Use the input.py file to dictate key parameter inputs (e.g. model extent) and run ‘fault_extraction_analysis_2D.py’ to execute the main script. Comments are provided in the script, but two jupyter notebooks provide an additional step-by-step context of the fault extraction workflow and reproduction of figures.(3) <b>Extracted fault statistics:</b> are the generated outputs from the Python scripts. ‘Geometric_relationships’ is the main excel database generated from ‘fault_extraction_analysis_2D.py’ whereby each row represents the data for one single fault (or label in python) and contains coordinates, strain, length etc. statistics. Alternatively there is an option to generate an equivalent excel file, but whereby each row represents an x, y coordinate (with the associated label represented as a unique accessible index) – this option works better for plotting the results and the option to generate this is given in the input.py script and jupyter notebooks.<br>In the publication, we plot (a) overall strain summation of the study area, (b) fault accommodated strain and (c) OFD (i.e. the difference between a and b). The excel data for each of these are present as subfolders, and were extracted from the main python script, as well as additional scripts 'results_concat.py’ and ‘results_to_strain_time.py’ which process the excel files in ‘Geometric_relationships’. The calculation and plotting of OFD is shown in the jupyter notebook ‘OFF FAULT DEFORMATION calculations.ipynb’.
DOI: 10.5281/zenodo.8290722
title: Datasets for the Comparative Restriction Enzyme Analysis of Methylation (CREAM) in cannabis clonal lines

description: These are the datasets used for the Comparative Restriction Enzyme Analysis of Methylation (CREAM) in cannabis clonal lines.
DOI: 10.5281/zenodo.8290721
title: Datasets for the Comparative Restriction Enzyme Analysis of Methylation (CREAM) in cannabis clonal lines

description: These are the datasets used for the Comparative Restriction Enzyme Analysis of Methylation (CREAM) in cannabis clonal lines.
DOI: 10.17595/20230830.001
title: O2/N2 ratios and CO2 mole fractions of air samples collected at Cape Ochiishi

description: The present data set is time series data of atmospheric O2/N2 ratios and CO2 mole fractions from air samples collected at Cape Ochiishi situated at the base of Nemuro Peninsula in eastern Hokkaido, Japan. There is a monitoring station on the top of a cliff 42.4 m above the sea level at the southern tip of the cape facing the Pacific Ocean. Air samples, drawn by a pump from an intake placed on a tower at a height of 51.5 m above ground level, are forced into Pyrex glass flasks. The collected air samples are sent back to a laboratory in NIES, where O2/N2 ratios and CO2 mole fractions are measured.
DOI: 10.17595/20230830.002
title: O2/N2 ratios and CO2 mole fractions of air samples collected at Hateruma Island

description: The present data set is time series data of atmospheric O2/N2 ratios and CO2 mole fractions from air samples collected on Hateruma Island, Japan. Hateruma Island is a small ellipse-shaped island in east-west direction with an area of 13 km2 and is the southwestern-most inhabited island of the Japanese archipelago with a population of 500. The monitoring station is located at the eastern tip of the island. Air samples, drawn by a pump from an intake placed on a tower at a height of 37 m above ground level, are forced into Pyrex glass flasks. The collected air samples are sent back to a laboratory in NIES, where O2/N2 ratios and CO2 mole fractions are measured.
DOI: 10.17595/20230830.003
title: O2/N2 ratios and CO2 mole fractions of air samples collected on Minamitorishima

description: The present data set is time series data of atmospheric O2/N2 ratios and CO2 mole fractions from air samples collected at a monitoring station on Minamitorishima, Japan. Air samples, drawn by a pump from an intake placed on a tower at a height of 20 m above ground level, are forced into Pyrex glass flasks. The collected air samples are sent back to a laboratory in NIES, where O2/N2 ratios and CO2 mole fractions are measured.
DOI: 10.17595/20230830.004
title: O2/N2 ratios and CO2 mole fractions of air samples collected onbaord cargo ships sailing in the Pacific region

description: The present data set is data of atmospheric O2/N2 ratios and CO2 mole fractions of flask samples collected onboard cargo ships during regular roundtrip cruises to North America, New Zealand/Australia, and Northeast Asia. Air samples, drawn by a pump from intakes placed on bow mast or at the the top of the bridge of the cargo ships, are forced into Pyrex glass flasks. The collected air samples are sent back to a laboratory in NIES, where O2/N2 ratios and CO2 mole fractions are measured.
DOI: 10.17597/isas.darts/hyb2-00200
title: Hayabusa2 Optical Navigation Camera (ONC) Bundle

description: This PDS4 bundle collects all the operational data products produced by the Hayabusa2 ONC instrument.
DOI: 10.17597/isas.darts/hyb2-00800
title: Hayabusa2 MASCOT Camera Bundle

description: This PDS4 bundle collects all the operational data products produced by the Hayabusa2 MASCOT Camera, MASCAM.
DOI: 10.5281/zenodo.7763311
title: Investigating the Use of AI-Generated Exercises for Beginner and Intermediate Programming Lectures: A ChatGPT Case Study

description: In recent years, artificial intelligence (AI) has been increasingly used in education and supports teachers in creating educational material and students in their learning progress. AI- driven learning support has recently been further strengthened by the release of ChatGPT, in which users can retrieve expla- nations for various concepts in a few minutes through chat. However, to what extent the use of AI models, such as ChatGPT, is suitable for the creation of didactically and content-wise good exercises for programming courses is not yet known. Therefore, in this paper, we investigate the use of AI-generated exercises for beginner and intermediate programming courses in higher education using ChatGPT. We created 12 exercise sheets with ChatGPT for a beginner to intermediate programming course focusing on the objects-first approach. We report our process, prompts, and experience using ChatGPT for this task and outline good practices we identified. The generated exercises are assessed and revised, primarily using ChatGPT, until they met the requirements of the programming course. We assessed the quality of these exercises by using them in our course as external teaching assignment at the University of Education Ludwigsburg and let the students evaluate them. Results indicate the quality of the generated exercises and the time-saving for creating them using ChatGPT. However, our experience showed that while it is fast to generate a good version of an exercise, almost every exercise requires minor manual changes to improve its quality.
DOI: 10.5517/ccdc.csd.cc2d62c2
title: CCDC 2210373: Experimental Crystal Structure Determination

description: Related Article: Xue Bai, Xu Han, Yuxin Wang, Ange Zhang, Yanli Yang, Ying Lu, Shuxia Liu|2023|Inorg.Chem.|62|13221|doi:10.1021/acs.inorgchem.3c01393
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.cc2f8b8b
title: CCDC 2242331: Experimental Crystal Structure Determination

description: Related Article: Xiaodan Jia, Zhigang Tang, Lina Meng, Zixu Wang, Dan Wang, Lijuan Chen, Junwei Zhao|2023|Inorg.Chem.|62|13639|doi:10.1021/acs.inorgchem.3c02111
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: 10.25345/c5513v612
title: MassIVE MSV000092784 - In vitro phosphorylation of trypsin-digested K562 cell lysate by TAM family kinases
title: K562 cell lysate was digested with trypsin, dephosphorylated with lambda phosphatase, and treated in an in vitro kinase reaction with Axl, Mer or Tyro3 kinase, alongside a control reaction for each that contained no kinase. Samples were phosphoenriched with the SMOAC approach (TiO2 and FeNTA) and analyzed on an Orbitrap-Velos. Peptides were identified using PEAKS Studio X Pro.
DOI: 10.5517/ccdc.csd.cc2gqrb9
title: CCDC 2285981: Experimental Crystal Structure Determination

description: Related Article: J. Sung|2023|IUCrData|8|x230668|doi:10.1107/S2414314623006685
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.cc2gkd8q
title: CCDC 2280833: Experimental Crystal Structure Determination

description: Related Article: P. Ganesan, N.J. Jasmine, R.S. Darious, K. Soundararajan, R. Rajalingam|2023|IUCrData|8|x230674|doi:10.1107/S2414314623006740
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: 10.25934/pr00003130.11
title: Available IPD datapackage for study 'Randomized Clinical Trial Comparing Low Power (50W) Holmium Laser Enucleation of the Prostate (HoLEP) Versus High Power (100W) HoLEP'

description: HoLEP (Holmium laser enucleation of the prostate) entails dissection of the whole median and lateral prostatic lobes off the surgical capsule via a retrograde approach starting at the apex. The enucleated lobes were pushed to the bladder followed by hemostasis of the prostate bed then intravesical morcellation of the enucleated adenoma. The investigators are going to compare safety and efficacy of low power; LP-HoLEP vs. high power; HP-HoLEP in treatment of infra-vesical obstruction secondary to BPH (Benign prostate Hyperplasia).
DOI: 10.6073/pasta/d4e55fd828cf5f2f9ae331373998c140
title: Water Temperatures and Climatology for Wachapreague, VA, 1982-2021

description: Daily average measured near-surface water temperature from 1982-2021 constructed for the site in the Virginia coastal bays where the NOAA Wachapreague station is located (Site CBW (Coastal Bay Wachapreague); 37.61N, 75.69W; water depth 1m) and for a site in the coastal ocean just outside the bays (Site COC (Coastal Ocean CHLV2); 36.91N, 75.71W; water depth 15m). Daily climatological mean temperature and marine heatwave threshold (90th percentile) calculated using 30 years of the record (1987-2016) are also provided.
DOI: 10.17608/k6.auckland.24037866
title: NZ Broadcasting Standards Authority decision data

description: Spreadsheets containing data relating to complaints to the Broadcasting Standards Authority and subsequent decisions issued by the Authority. The original request for data was made to the BSA on 5 August 2023 for aggregated decision data appearing in the public Decisions database. The BSA sent a dataset containing decision data created under their current codebook which came into effect 1 July 2022 (<code>20230807 AllClosedFormalComplaints.xlsx</code>). A subsequent request was made for the equivalent data created under the previous BSA codebook, which was in effect between 1 April 2016 to 30 June 2022, and the BSA provided this (<code>20230810 AllClosedFormalComplaints.xlsx</code>).The BSA is an independent Crown entity not subject to the NZGOAL framework, but agreed to allow reuse of the data on terms equivalent to NZGOAL (Creative Commons Attribution 4.0).
DOI: 10.6084/m9.figshare.24053385
title: Restoration techniques

description: These data has been collected in study area for testing some retoration techniques
DOI: 10.6084/m9.figshare.24053385.v1
title: Restoration techniques

description: These data has been collected in study area for testing some retoration techniques
DOI: 10.25904/1912/6
title: Findings from the Seeding the Data Commons Project

description: This dataset includes a detailed survey that collected information on the types of research data produced at the University, the kinds of storage practices used for maintaining research data, how the research data is managed, what access permissions are in place or are required and any legal requirements in respect to the data. Anonymised survey data results are included. The Griffith University's Seeding the Data Commons (NCRIS) project gathered information professionals with specialised areas in information management, innovative librarianship and technical architecture, to develop a uniform approach to identifying, capturing and exposing research data, while honouring the key principles set forth in the Griffith University Code for the Responsible Conduct of Research. Specific actions conducted by the Seeding the Data Commons (NCRIS) project team included: I. Identified and described research data associated with Griffith research projects in order to meet the criteria set out by the Australian Research Data Commons and the National Collaborative Research Infrastructure Strategy. II. Partnered with the Griffith Office for Research to extract research project metadata (project funding sponsors, project status, project description, project leader/manager, project identifier) from internal research systems, which was then migrated to a Griffith institutional research repository in RIF-CS Format. III. Assessed related datasets and identified appropriate access requirements (open, ediated, controlled, metadata only, restricted) in line with Federal, State and Griffith's data management policies. IV. Catalogued appropriate metadata pertaining to research metadata stores using the Registry Interchange Format - Collections and Services (RIF-CS) Schema, the data interchange format supporting Research Data Australia. V.Migrated Griffith's RIF-CS records directly from a Griffith institutional research repository to the Australian Research Data Commons (ARDC) using cross walking techniques, and piloted test feeds via XML push technology and the use of the OAI-PMH harvest protocol (via the EIF project) VI. Accommodated any legal, ethical, security or other constraints over the lifecycle of research data identified via direct communication with the Office for Research and research participants at Griffith University.
DOI: 10.34740/kaggle/dsv/6386154
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.5281/zenodo.8277159
title: Human Inner Ear Anatomy: Labeled Volume CT Data of Inner Ear Fluid Space and Anatomical Landmarks

description: The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of \(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\). Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm. <strong>Usage Notes</strong> The datasets are formatted in the HDF5 format developed by the HDF5 Group. We utilized and thus recommend the usage of Python bindings pyHDF to handle the datasets. The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets: <pre><code>raw/raw-0 label/label-0 landmark/landmark-0 landmark/landmark-1 landmark/landmark-2</code></pre> Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets. Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows: <pre><code class="language-python">{'coordsys': 'LPS', 'id': 1, 'ijk_position': array([181, 188, 100]), 'label': 'CochleaTop', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}</code></pre> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 2, 'ijk_position': array([222, 182, 145]), 'label': 'OvalWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}</code></pre> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 3, 'ijk_position': array([223, 209, 147]), 'label': 'RoundWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}</code></pre>
description: This research project received support from the Bavarian Ministry of Economic Affairs, Infrastructure, Transport and Technology as well as from the Interdisziplinäres Zentrum für Klinische Forschung der Universität Würzburg, Grant Z_4/150-200. D.M. Pelt is supported by The Netherlands Organization for Scientific Research (NWO), project number 016.Veni.192.235