cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14278/rodare.2258
title: Data: Particle dispersions 3D characterization of chromite ore particles with different sizes

description: Particle dispersions for 3D analysis using computed tomography prepared according to a standardized sample preparation procedure. Particles are from a Chromite ore (Kemi mine). Each sample has a specific size class. Analysis of the data a published open source
DOI: 10.5281/zenodo.10016397
title: YourMT3+ dataset (restricted)

description: This dataset is limited to internal projects.
DOI: 10.5281/zenodo.8417803
title: YourMT3+ dataset (restricted)

description: This dataset is limited to internal projects.
DOI: 10.5281/zenodo.8417804
title: YourMT3+ dataset (restricted)

description: This dataset is limited to internal projects.
DOI: 10.5281/zenodo.8436930
title: YourMT3+ dataset (restricted)

description: This dataset is limited to internal projects.
DOI: 10.6084/m9.figshare.24325073
title: Potential intensification of permafrost carbon feedback under net-zero and negative emissions

description: Using the CESM2 model, a simulated dataset was created to investigate the response of the permafrost carbon cycle to net-zero and negative emissions through an emission-driven carbon dioxide removal simulation in the study titled <i>"</i><i>Potential intensification of permafrost carbon feedback under net-zero and negative emissions</i><i>".</i>
DOI: 10.5281/zenodo.10008180
title: Artifact: SoK: Evaluations in Industrial Intrusion Detection Research

description: This collection resembles the artifact of our publication "SoK: Evaluations in Industrial Intrusion Detection Research," published in the Journal of Systems Research 2023, in which we performed a systematic mapping study on the literature of Industrial Intrusion Detection Systems (IIDSs). Within this paper, we systematically analyzed the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. This artifact contains our data extracted from the 609 publications under consideration in the survey. For further information, please refer to the respective publication.
DOI: 10.5281/zenodo.10008179
title: Artifact: SoK: Evaluations in Industrial Intrusion Detection Research

description: This collection resembles the artifact of our publication "SoK: Evaluations in Industrial Intrusion Detection Research," published in the Journal of Systems Research 2023, in which we performed a systematic mapping study on the literature of Industrial Intrusion Detection Systems (IIDSs). Within this paper, we systematically analyzed the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. This artifact contains our data extracted from the 609 publications under consideration in the survey. For further information, please refer to the respective publication.
DOI: 10.5281/zenodo.7793404
title: MusicNet-16k + EM for YourMT3

description: <strong>About this version:</strong> This particular variant of the MusicNet dataset has been resampled to a 16 kHz-mono-16-bit-wav format, which makes it more suitable for certain audio processing tasks, particularly those that require lower sampling rates. We redistribute this data as a part of YourMT3 project. The license for redistribution is attached. Moreover, this version of the dataset includes various split options derived from previous works on automatic music transcription as python dictionary (see README.md). Below is a brief description of available split options: <code>train_mt3</code>: The first 300 songs are from a synth dataset, while the remaining 300 songs are from an acoustic dataset. <code>train_mt3_synth</code>: The training set of the MusicNet MIDI recordings split by MT3 author's split. This is not the synthetic dataset of EM (MIDI Pop 80K) nor pitch-augmented. <code>train_mt3_acoustic</code>: Acoustic recordings from the MT3 author's split. <code>validation_mt3</code>: A set of 15 validation recordings. <code>validation_mt3_synth</code>: A set of 15 synthetic validation recordings. <code>validation_mt3_acoustic</code>: A set of 15 acoustic validation recordings. <code>test_mt3_acoustic</code>: A set of 15 acoustic test recordings. <code>train_thickstun</code>: The first 327 songs are from a synth dataset, while the remaining 327 songs are from an acoustic dataset. <code>test_thickstun</code>: A set of 3 test recordings. <code>train_em_table2</code>: 323 tracks, excluding 7 missing tracks and 6 test_em tracks. <code>validation_em</code>: A set of 15 validation recordings. <code>test_em_str_table2</code>: A set of 4 string test recordings, using EM annotations. <code>test_em_wind_table2</code>: A set of 2 woodwind test recordings, using EM annotations. <code>test_cheuk_str_table2</code>: A set of 4 string test recordings, using Thickstun's annotations. <code>test_cheuk_wind_table2</code>: A set of 2 woodwind test recordings, using Thickstun's annotations. <strong>About MusicNet:</strong> The MusicNet dataset, originally released in 2016 by Thickstun et al., "Learning Features of Music from Scratch". It is a collection of music recordings annotated with labels for various tasks, such as automatic music transcription, instrument recognition, and genre classification. The original dataset contains over 330 hours of audio, sourced from various public domain recordings of classical music, and is labeled with instrument activations and note-wise annotations. <strong>About MusicNet EM:</strong> MusicNetEM are refined labels for the MusicNet dataset, in the form of MIDI files. They are aligned with the recordings, with onset timing within 32ms. They were created using an EM process, similar to the one described in the Ben Maman and Amit H. Bermano, "Unaligned Supervision for Automatic Music Transcription in The Wild". Their split (Table 2 of this paper) derived from another paper, Kin Wai Cheuk et al., "ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data". <strong>License:</strong> CC-BY-4.0
DOI: 10.5281/zenodo.7793420
title: MusicNet-16k + EM for YourMT3

description: <strong>About this version:</strong> This particular variant of the MusicNet dataset has been resampled to a 16 kHz-mono-16-bit-wav format, which makes it more suitable for certain audio processing tasks, particularly those that require lower sampling rates. We redistribute this data as a part of YourMT3 project. The license for redistribution is attached. Moreover, this version of the dataset includes various split options derived from previous works on automatic music transcription as python dictionary (see README.md). Below is a brief description of available split options: <code>train_mt3</code>: The first 300 songs are from a synth dataset, while the remaining 300 songs are from an acoustic dataset. <code>train_mt3_synth</code>: The training set of the MusicNet MIDI recordings split by MT3 author's split. This is not the synthetic dataset of EM (MIDI Pop 80K) nor pitch-augmented. <code>train_mt3_acoustic</code>: Acoustic recordings from the MT3 author's split. <code>validation_mt3</code>: A set of 15 validation recordings. <code>validation_mt3_synth</code>: A set of 15 synthetic validation recordings. <code>validation_mt3_acoustic</code>: A set of 15 acoustic validation recordings. <code>test_mt3_acoustic</code>: A set of 15 acoustic test recordings. <code>train_thickstun</code>: The first 327 songs are from a synth dataset, while the remaining 327 songs are from an acoustic dataset. <code>test_thickstun</code>: A set of 3 test recordings. <code>train_em_table2</code>: 323 tracks, excluding 7 missing tracks and 6 test_em tracks. <code>validation_em</code>: A set of 15 validation recordings. <code>test_em_str_table2</code>: A set of 4 string test recordings, using EM annotations. <code>test_em_wind_table2</code>: A set of 2 woodwind test recordings, using EM annotations. <code>test_cheuk_str_table2</code>: A set of 4 string test recordings, using Thickstun's annotations. <code>test_cheuk_wind_table2</code>: A set of 2 woodwind test recordings, using Thickstun's annotations. <strong>About MusicNet:</strong> The MusicNet dataset, originally released in 2016 by Thickstun et al., "Learning Features of Music from Scratch". It is a collection of music recordings annotated with labels for various tasks, such as automatic music transcription, instrument recognition, and genre classification. The original dataset contains over 330 hours of audio, sourced from various public domain recordings of classical music, and is labeled with instrument activations and note-wise annotations. <strong>About MusicNet EM:</strong> MusicNetEM are refined labels for the MusicNet dataset, in the form of MIDI files. They are aligned with the recordings, with onset timing within 32ms. They were created using an EM process, similar to the one described in the Ben Maman and Amit H. Bermano, "Unaligned Supervision for Automatic Music Transcription in The Wild". Their split (Table 2 of this paper) derived from another paper, Kin Wai Cheuk et al., "ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data". <strong>License:</strong> CC-BY-4.0
DOI: 10.6084/m9.figshare.24331177
title: Luo et al_2023.xlsx

description: The data of temperature sensitivity of soil microbial respiration on QTP.
DOI: 10.6084/m9.figshare.24331177.v1
title: Luo et al_2023.xlsx

description: The data of temperature sensitivity of soil microbial respiration on QTP.
DOI: 10.6084/m9.figshare.24331189
title: Statistical analysis of qPCR data that reveal the existence of intron-3 retaining <i>MAPT</i> mature RNA species

description: Statistical analysis of qPCR data that reveal the existence of intron-3 retaining MAPT mature RNA species
DOI: 10.6084/m9.figshare.24331189.v1
title: Statistical analysis of qPCR data that reveal the existence of intron-3 retaining <i>MAPT</i> mature RNA species

description: Statistical analysis of qPCR data that reveal the existence of intron-3 retaining MAPT mature RNA species
DOI: 10.6084/m9.figshare.24331195
title: Dofile Study: Affectedness, partisanship and evaluations of containment measures:The formation of political trust during the COVID-19 pandemic in Germany

description: The outbreak of the COVID-19 pandemic triggered ‘rally around the flag’ effects in various countries across Europe. There has been little research examining the relative relevance of political trust determinants in relation to different phases of the pandemic. This article aims to explore what role conventional determinants, objective affectedness and perceptions of containment measures played in explaining levels of political trust in Germany over the course of the pandemic. By employing OLS and FE regressions in a three-wave panel survey, I find that party identifications and evaluation of containment measures played decisive roles, while experienced income losses did not robustly shape political trust between 2020 and 2021. The trend analysis indicates that threat perceptions of the impact of containment measures on German society – but not on individuals – increasingly eroded trust in politics across various subgroups of respondents as the partisan trust gap narrowed over time.
DOI: 10.5281/zenodo.10016480
title: dbgi_tropical_minitoydataset

description: Same as https://zenodo.org/records/8428675 with less (three) samples
DOI: 10.5281/zenodo.10016479
title: dbgi_tropical_minitoydataset

description: Same as https://zenodo.org/records/8428675 with less (three) samples
DOI: 10.6084/m9.figshare.24331519
title: Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects

description: It accompanies the journal article submission entitled "Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects".
DOI: 10.6084/m9.figshare.24331519.v1
title: Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects

description: It accompanies the journal article submission entitled "Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects".
DOI: 10.25451/flinders.24248698
title: Gregory D. Kerr & Grant A. Gully (2023) Bird communities and effects of management in Heathy Woodlands in the Grampians/Gariwerd National Park. <i>Online Data</i>: GGBS_Data_GK_AllSurveysComb_Dec2021.xlsx

description: Online data for the paper:<br>Gregory D. Kerr & Grant A. Gully (2023) Bird communities and effects of management in Heathy Woodlands in the Grampians/Gariwerd National Park, Transactions of the Royal Society of South Australia, DOI: 10.1080/03721426.2023.2265638<br>
DOI: 10.25451/flinders.24248698.v1
title: Gregory D. Kerr & Grant A. Gully (2023) Bird communities and effects of management in Heathy Woodlands in the Grampians/Gariwerd National Park. <i>Online Data</i>: GGBS_Data_GK_AllSurveysComb_Dec2021.xlsx

description: Online data for the paper:<br>Gregory D. Kerr & Grant A. Gully (2023) Bird communities and effects of management in Heathy Woodlands in the Grampians/Gariwerd National Park, Transactions of the Royal Society of South Australia, DOI: 10.1080/03721426.2023.2265638<br>
DOI: 10.11588/data/qjezht/5sawe7
title: RScript_PCA_XRF.txt

description: :unav
DOI: 10.11588/data/qjezht/hofaz3
title: FLUGARTH_Data_heiDATA_v2.tab

description: Update: Version 2
DOI: 10.11588/data/qjezht/ryaxou
title: RScript_BACON.txt

description: :unav
DOI: 10.11588/data/qjezht/vggb02
title: FLUGARTH_Data_heiDATA.tab

description: :unav
DOI: 10.5281/zenodo.10016507
title: dbgi_tropical_mini_toy_dataset

description: The same as https://zenodo.org/records/8428675 with less (three) samples
DOI: 10.5281/zenodo.10016506
title: dbgi_tropical_mini_toy_dataset

description: The same as https://zenodo.org/records/8428675 with less (three) samples
DOI: 10.6084/m9.figshare.24331093
title: Table Code-Co-Occurence Analysis for the Study "Exploring Critical Learning Incidents in Collaborative Online International Learning"

description: Altmann, M., & Arnold, M. (2023). Exploring Critical Learning Incidents in Collaborative Online International Learning: Implications for Digital Readiness and Learning Design, Research Paper. In Proceeding of the Gemeinschaft in Neuen Medien (GeNeMe) Conference 2023 – Inclusive Digital: Forming Community in an Open Way - Self-determined Participation in the Digital Transformation. Dresden TUDpress.
DOI: 10.6084/m9.figshare.24331093.v1
title: Table Code-Co-Occurence Analysis for the Study "Exploring Critical Learning Incidents in Collaborative Online International Learning"

description: Altmann, M., & Arnold, M. (2023). Exploring Critical Learning Incidents in Collaborative Online International Learning: Implications for Digital Readiness and Learning Design, Research Paper. In Proceeding of the Gemeinschaft in Neuen Medien (GeNeMe) Conference 2023 – Inclusive Digital: Forming Community in an Open Way - Self-determined Participation in the Digital Transformation. Dresden TUDpress.
DOI: 10.6084/m9.figshare.24333232
title: Data

description: Data from the article "Elevation modulates the impacts of climate change on the Brazilian Cerrado flora"
DOI: 10.6084/m9.figshare.24333232.v1
title: Data

description: Data from the article "Elevation modulates the impacts of climate change on the Brazilian Cerrado flora"
DOI: 10.21227/hyf5-3c15
title: Real-Time Landslide Dataset of Mawiongrim, Meghalaya, India

description: This paper presents a bi-directional Long ShortTerm Memory (LSTM) model for the detection of landslides. Previous uses of machine learning in this setting have demonstrated its general potential, which necessitates the implementation of a suitable algorithm. Landslides are natural disasters that can cause significant destruction and disruption in the affected areas. Early detection is the key to minimizing the impact of landslides, so it is important to develop accurate and efficient models. An area selected for this study is located in Mawiongrim, Meghalaya, India, which is an active landslide zone. The proposed model uses a bi-directional LSTM to capture the temporal patterns of the input data collected from a long-term real-time monitoring system set up in the area. To evaluate the effectiveness of the predictions, the model is trained using a dataset composed of various landslide-related characteristics, such as topography, rainfall, hydrological and soil properties. The results show that the suggested model is capable of detecting landslides with greater accuracy and the lowest error value relative to other models. Additionally, the model is also able to provide a real-time warning system, making it a viable tool for early landslide detection. The research also highlights the prediction models for matric suction and groundwater level, which are crucial in determining slope stability
DOI: 10.21227/35xc-s740
title: Cross Talking TDC Demo

description: A Pulse Stretching Time to Digital Converter(TDC) exploits the crosstalk effect on FPGA. The principle idea is to superpose an induction voltage on the sense line to lower its logic toggling voltage while maintaining the other parameters intact. The focus of this cross-talking TDC is to ameliorate the delay variation caused by temperature effects on conventional TDCs. Theoretical analysis regarding temperature stability on pass-transistors, track buffers and metal wires are conducted. Detailed calculations are performed using an α power law based transition delay model with device parameters obtained from previous studies.At the same time, the impact of error accumulation on series connected transistor line is emphasized. To compare its magnitude with a cross talk stretched interconnect, a numerical assessment is performed.  In order to bridge the gap between theoretical analysis and practical implementations,  several design strategies and delay adjustment methods are presented.   Experiments have been conducted at different temperatures. And their single shot-precision, non-linearity, and measurement range have all reached the state-of-the-art. Surely, improvements in temperature stability has been confirmed.
DOI: 10.25345/c5wp9th8d
title: MassIVE MSV000093137 - Identification of non-conventional small molecule degraders and stabilizers of squalene synthase
title: The dataset was obtained by performing a TMTpro16plex expression proteomics experiment, where HeLa cells were treated with small molecule ligands of squalene synthase (FDFT1). The experiment was successful in re-producing previously obtained Western Blot results in regards to SQS (=FDFT1) levels and aimed at elucidating downstream effects on cholesterol biosynthesis and metabolism. The dataset comprises data for the following samples: DMSO (vehicle), KY02111, PROTAC 18, SQSI and a compound unrelated to this project (TMTpro16plex, processed by Maxquant, each collected in 30 fractions). The experiments were performed in three independent replicates for each compound (R1-R3).
DOI: 10.6084/m9.figshare.24333844
title: Mouse TF motif

description: The motif of mouse TF.
DOI: 10.6084/m9.figshare.24333844.v1
title: Mouse TF motif

description: The motif of mouse TF.
DOI: 10.6084/m9.figshare.24299131
title: example dataset to fit PLDS model, see Haimerl et al. 2023, previously published in Ruff & Cohen 2016

description: Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.
DOI: 10.6084/m9.figshare.24299131.v1
title: example dataset to fit PLDS model, see Haimerl et al. 2023, previously published in Ruff & Cohen 2016

description: Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.
DOI: 10.17632/nr9sx8by4f.1
title: Di- and tri-methylation of histone H3K36 play distinct roles in different DNA double-strand break repair pathways

description: 16 excel file including original image data and digital data in each figure.
DOI: 10.17632/37b9hg5mg7.1
title: VARSew: A Visual Action Recognition Dataset on Garment Sewing

description: VARSew dataset is designed for visual action recognition in the garment sewing industry. Also, VARSew is designed to address multiple research objectives in visual recognition, such as binary and multi-class action classification. The proposed data offers new research opportunities, such as visual pattern recognition and Internet-of-Things(IOT) based manufacturing. Also, it offers research opportunities on human activity and behavior during production time. To the best of our knowledge, no such industrial human action dataset is available in the computer vision community. VARSew dataset consists of high-resolution trimmed videos of certain actions. There are two levels of categorical labels in the VARSew dataset, i.e., super action classes and actions classes. The dataset includes 3,121 videos of 49,936 frames. The videos are grouped into value-added and non-value-added for human sewing action binary classification. For multi-class classification, the videos are also grouped into eight classes: sew, release, handle, prepare, adjust, wait, check, and maintain. Each video was fixed to 16 frames. Multiple human operators were employed to collect the videos, including Sewing Machine Operators (SMO) and Maintenance Machine Operators (MMO), respectively 26 and 8 operators. Subjective annotations for the participant proficiency throughout the 3,121 videos.
DOI: 10.17632/zfnj7zjhc5.1
title: HDAC inhibitors activate lipid peroxidation and ferroptosis in gastric cancer Supplementary

description: Supplementary files for above mentioned research article
DOI: 10.17632/v7r3dsgtmz.1
title: Chapter 9 - Advanced Feature Engineering Techniques for Fraud Analytics

description: Dataset_1:
The dataset consists of transaction timestamps (in hours) for a sample of online banking transactions. The timestamps represent the time of day when the transactions occurred.

Dataset_2:
The dataset, encapsulated as a pandas DataFrame "trans_David", chronicles the transactional activities of an individual named David. A salient column, "channel_cd", signifies the payment channel employed by David for each transaction. The dataset encompasses 40 entries across 14 columns, with 'channel_cd' being the focal point for the derivation of the 'freq_channel' feature.