cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rg353wcrwr.1
title: CD147 regulates CD8+ T cell cytotoxic activity by facilitating phosphorylation and subsequent recruitment of proximal TCR signaling kinases(ISCIENCE-D-25-09187)

description: Doxycycline Inducible Chimeric Antigen Receptor T Cells Targeting CD147 for Hepatocellular Carcinoma Therapy
DOI: 10.17632/4nww8p6vxf
title: Battery_expansion_force_data

description: The experimental dataset includes one prismatic cell (Gotion-IFP4014888-53Ah) and one 1P10S module composed of 10 such prismatic cells connected in series. Both underwent 1000 cycles of aging, with capacity calibration performed once every 50 cycles. Within the parent folder Battery_expansion_force_data, there are two subfolder named Cell_Data(All electrical performance and expansion force data of the cell) and Module_Data(All electrical performance and expansion force data of the Module). The Cell_Data folder contains four subfolders: Cycle_Ageing_Cell_data, Capacity_Calibration_Cell_data, Expansion_force_Cell_source_data, and Expansion_force_Cell_feature_data. Similarly, the Module_Data folder also contains four subfolders: Cycle_Ageing_Module_data, Capacity_Calibration_Module_data, Expansion_force_Module_source_data, and Expansion_force_Module_feature_data.The data formats of the four groups under Cell and Module are identical. The following uses the Cell data tables as an example for file description:(i) The Cycle_Ageing_Cell_data folder contains raw electrical performance data of the battery during the 1000-cycle aging process. It includes 31 data files. Taking the data file named "Report__BT1_MCU12_2024-11-12_17.07.32" as an example: "2024-11-12_17.07.32" represents the time when the test occurred. All files, sorted chronologically, record all charge-discharge electrical performance parameter values for the 1000 cycles. In each of the 31 data files, the parameters in each column in order are: test time, test step, total energy, discharge energy, charge energy, real-time current, real-time power, total capacity, discharge capacity, charge capacity, battery surface temperature, and battery voltage.(ii) The Capacity_Calibration_Cell_data file contains a summary statistics table of the calibrated capacity and SOH measured after every 50 cycles.(iii) The Expansion_force_Cell_source_data folder contains the expansion force distribution matrix dataset on the main surface of the battery during the 1000-cycle aging process. The explanation of its contents is as follows: each frame is recorded in chronological order with a recording interval of 30 seconds. The "64x64 table" in each frame represents the pressure values collected by the thin-film pressure sensor per unit area of 0.04 cm², totaling 4096 values. This represents the force distribution over a plane area of 163.84 cm² (Note: the actual plane area of the cell from which expansion force is collected is only 130.24 cm². Therefore, only a portion of the collection points have numerical values, while other points not collecting force have a value of 0).(iv) The Expansion_force_Cell_feature_data file contains a summary statistics table of expansion force features, this includes: number of cycles, the start and end times of charging and discharging, temperature, the expansion force values at the start and end of charging and discharging, and the maximum and minimum values during the process.
DOI: 10.17632/4nww8p6vxf.1
title: Battery_expansion_force_data

description: The experimental dataset includes one prismatic cell (Gotion-IFP4014888-53Ah) and one 1P10S module composed of 10 such prismatic cells connected in series. Both underwent 1000 cycles of aging, with capacity calibration performed once every 50 cycles. Within the parent folder Battery_expansion_force_data, there are two subfolder named Cell_Data(All electrical performance and expansion force data of the cell) and Module_Data(All electrical performance and expansion force data of the Module). The Cell_Data folder contains four subfolders: Cycle_Ageing_Cell_data, Capacity_Calibration_Cell_data, Expansion_force_Cell_source_data, and Expansion_force_Cell_feature_data. Similarly, the Module_Data folder also contains four subfolders: Cycle_Ageing_Module_data, Capacity_Calibration_Module_data, Expansion_force_Module_source_data, and Expansion_force_Module_feature_data.The data formats of the four groups under Cell and Module are identical. The following uses the Cell data tables as an example for file description:(i) The Cycle_Ageing_Cell_data folder contains raw electrical performance data of the battery during the 1000-cycle aging process. It includes 31 data files. Taking the data file named "Report__BT1_MCU12_2024-11-12_17.07.32" as an example: "2024-11-12_17.07.32" represents the time when the test occurred. All files, sorted chronologically, record all charge-discharge electrical performance parameter values for the 1000 cycles. In each of the 31 data files, the parameters in each column in order are: test time, test step, total energy, discharge energy, charge energy, real-time current, real-time power, total capacity, discharge capacity, charge capacity, battery surface temperature, and battery voltage.(ii) The Capacity_Calibration_Cell_data file contains a summary statistics table of the calibrated capacity and SOH measured after every 50 cycles.(iii) The Expansion_force_Cell_source_data folder contains the expansion force distribution matrix dataset on the main surface of the battery during the 1000-cycle aging process. The explanation of its contents is as follows: each frame is recorded in chronological order with a recording interval of 30 seconds. The "64x64 table" in each frame represents the pressure values collected by the thin-film pressure sensor per unit area of 0.04 cm², totaling 4096 values. This represents the force distribution over a plane area of 163.84 cm² (Note: the actual plane area of the cell from which expansion force is collected is only 130.24 cm². Therefore, only a portion of the collection points have numerical values, while other points not collecting force have a value of 0).(iv) The Expansion_force_Cell_feature_data file contains a summary statistics table of expansion force features, this includes: number of cycles, the start and end times of charging and discharging, temperature, the expansion force values at the start and end of charging and discharging, and the maximum and minimum values during the process.
DOI: 10.17632/4spjvszr6z
title: Integrated Continual Learning Frameworks for Real-Time Combined Sewer Overflow Control Prediction and Optimization via Neural Inversion

description: These data form part of a research submission and primarily contain the outputs generated by the conducted study. The research presents an integrated framework for managing Combined Sewer Overflows (CSOs) in Detroit’s Puritan–Fenkell/Seven-Mile Collection System (PFSMC). It addresses the challenge of climate non-stationarity, wherein evolving precipitation patterns reduce the reliability of traditional stationary models. The study employs Continual Learning (CL) strategies specifically regularization-based, memory-based, and Bayesian approaches to incrementally update deep neural network (DNN) surrogate models without requiring full retraining. This predictive framework is coupled with neural inversion to optimize real-time control (RTC) of sewer infrastructure, such as gates and pumps, with the objective of maximizing storage utilization and reducing overflow events.
DOI: 10.15468/dl.9bj8nz
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Rubus chingii",
 "Year 1980-2026"
 ]
}

 The dataset includes 118 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070958-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4spjvszr6z.1
title: Integrated Continual Learning Frameworks for Real-Time Combined Sewer Overflow Control Prediction and Optimization via Neural Inversion

description: These data form part of a research submission and primarily contain the outputs generated by the conducted study. The research presents an integrated framework for managing Combined Sewer Overflows (CSOs) in Detroit’s Puritan–Fenkell/Seven-Mile Collection System (PFSMC). It addresses the challenge of climate non-stationarity, wherein evolving precipitation patterns reduce the reliability of traditional stationary models. The study employs Continual Learning (CL) strategies specifically regularization-based, memory-based, and Bayesian approaches to incrementally update deep neural network (DNN) surrogate models without requiring full retraining. This predictive framework is coupled with neural inversion to optimize real-time control (RTC) of sewer infrastructure, such as gates and pumps, with the objective of maximizing storage utilization and reducing overflow events.
DOI: 10.17632/jbssggdfx6
title: catena DATA

description: data
DOI: 10.17632/jbssggdfx6.1
title: catena DATA

description: data
DOI: 10.15468/dl.hkq2vx
title: Occurrence Download

description: A dataset containing 339724 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aichryson dumosum, Allium platakisii, Andryala crithmifolia, Armeria berlengensis, Atlantica calathoides, Belgrandiella austriana, Bencomia brachystachya, Bencomia sphaerocarpa, Berberis maderensis, Beta patula, Bupleurum dianthifolium, Callina bulverii, Calotriton arnoldi, Canariella jandiaensis, Caseolus subcalliferus, Cecilioides eulima, Centaurea hymettia, Cheirolophus crassifolius, Cheirolophus metlesicsii, Cheirolophus santos-abreui, Crambe tamadabensis, Crambe wildpretii, Desmana moschata, Dianella thiesseana, Discula lyelliana, Discula tabellata, Discula tetrica, Echium handiense, Echium portosanctense, Erica maderensis, Fissidens azoricus, Frullania sergiae, Fulica cristata, Gallotia stehlini, Genista bocchierii, Geomitra grabhami, Geranium maderense, Goodyera macrophylla, Grammitis azorica, Helichrysum melitense, Helosciadium bermejoi, Hemicycla efferata, Hemicycla modesta, Hemicycla paeteliana, Hymenophyllum maderense, Idiomela subplicata, Jasminum azoricum, Limonium albomarginatum, Limonium sventenii, Lotus eremiticus, Lotus maculatus, Lotus pyranthus, Melanopsis penchinati, Micromeria acropolitana, Micromeria glomerata, Monilearia granostriata, Mustela lutreola, Naufraga balearica, Olisthopus inclavatus, Orthodontium gracile, Parabythinella malaprespensis, Pericallis malvifolia, Plagiochila papillifolia, Pleiomeris canariensis, Polystichum drepanum, Prespiana lacustris, Prespolitorea malaprespensis, Prespolitorea valvataeformis, Prionotropis rhodanica, Psylliodes luridipennis, Puffinus mauretanicus, Renea bourguignatiana, Salamandra lanzai, Salvia herbanica, Salvia veneris, Serratorotula acarinata, Sideritis cystosiphon, Silene nocteolens, Sinapidendron angustifolium, Sinapidendron rupestre, Speleomantes ambrosii, Speleomantes sarrabusensis, Spermophilus pygmaeus, Sphagnum nitidulum, Tacheocampylaea romagnolii, Testudodiscula testudinalis, Tetralophozia filiformis, Teucrium abutiloides, Theodoxus baeticus, Theodoxus valentinus, Thesium psilotocladum, Thesium subsucculentum, Trematodon perssoniorum, Zonites invitus, Zonites nautarum, Acis nicaeensis, Acrobolbus azoricus, Acrobolbus madeirensis, Aeonium gomerense, Algyroides marchi)",
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Specimen, Unknown evidence, Machine Observation, Occurrence evidence)",
 "Year is greater than or equal to 1971",
 "Year is less than or equal to 2024",
 "HasCoordinate is true"
 ]
}

 The dataset includes 339724 records from 446 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070942-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ktwz4ky4p3
title: When Monetary Policy Stops Working: Financial Stress and the Non-Linear Transmission Mechanism in India

description: This study employs quarterly macroeconomic and financial
data for India spanning approximately four decades, cover
ing the period from the mid-1980s to the early 2020s. The
choice of a long sample period allows the analysis to cap
ture multiple monetary policy regimes, financial cycles, and
episodes of systemic stress, thereby providing a comprehen
sive assessment of monetary policy transmission under vary
ing financial conditions.
DOI: 10.17632/ktwz4ky4p3.1
title: When Monetary Policy Stops Working: Financial Stress and the Non-Linear Transmission Mechanism in India

description: This study employs quarterly macroeconomic and financial
data for India spanning approximately four decades, cover
ing the period from the mid-1980s to the early 2020s. The
choice of a long sample period allows the analysis to cap
ture multiple monetary policy regimes, financial cycles, and
episodes of systemic stress, thereby providing a comprehen
sive assessment of monetary policy transmission under vary
ing financial conditions.
DOI: 10.17632/wzzjzkstyz
title: Dataset on households’ perceptions, waste sorting and contributions for improved domestic waste management in Vietnam: An application of Culture Tower and CVM

description: This research aims to advance the understanding of Vietnamese citizens’ perceptions of household waste management challenges and their behavioral responses, with particular emphasis on their willingness-to-pay for improving waste management services. To be specific, between June and July 2025, we employed a hybrid data collection methodology integrating digital and in-person techniques, utilizing a stratified random sampling approach combined with the contingent valuation method to survey 431 households across 8 communes/wards in Hai Phong City, Vietnam. The study utilized the Culture Tower (KAUC/KHUC Tower) to systematically design and collect comprehensive data encompassing four primary information categories: (1) perceptions and attitudes toward waste management challenges and satisfaction levels with current waste treatment services, (2) existing household waste separation behaviors and practices, (3) willingness-to-pay commitments for improved waste management services and underlying motivational factors, (4) sociodemographic and economic characteristics of respondents.
DOI: 10.17632/wzzjzkstyz.4
title: Dataset on households’ perceptions, waste sorting and contributions for improved domestic waste management in Vietnam: An application of Culture Tower and CVM

description: This research aims to advance the understanding of Vietnamese citizens’ perceptions of household waste management challenges and their behavioral responses, with particular emphasis on their willingness-to-pay for improving waste management services. To be specific, between June and July 2025, we employed a hybrid data collection methodology integrating digital and in-person techniques, utilizing a stratified random sampling approach combined with the contingent valuation method to survey 431 households across 8 communes/wards in Hai Phong City, Vietnam. The study utilized the Culture Tower (KAUC/KHUC Tower) to systematically design and collect comprehensive data encompassing four primary information categories: (1) perceptions and attitudes toward waste management challenges and satisfaction levels with current waste treatment services, (2) existing household waste separation behaviors and practices, (3) willingness-to-pay commitments for improved waste management services and underlying motivational factors, (4) sociodemographic and economic characteristics of respondents.
DOI: 10.17632/rs4vz8n3t5
title: A smartphone-based vibration dataset for induction motor fault diagnosis under different speed and load conditions

description: This dataset provides smartphone-based vibration measurements collected from a three-phase squirrel-cage induction motor operating under multiple fault conditions, rotational speeds, and load states. The primary objective of the dataset is to support research on low-cost and accessible condition monitoring and fault diagnosis of induction motors using inertial sensors embedded in consumer-grade smartphones.

Vibration signals were acquired using an iPhone 15 Pro Max equipped with a tri-axial accelerometer, with data recorded via the Sensor Play mobile application. Data were recorded along three raw acceleration axes (gx, gy, gz) as well as gravity-compensated linear acceleration components (guserx, gusery, guserz). All signals were sampled at 100 Hz, and each measurement corresponds to a continuous recording of 15 minutes. The smartphone was firmly attached to the motor housing using double-sided adhesive tape to ensure consistent coupling during data acquisition.

The dataset includes six motor health conditions:
(i) Healthy operation (H),
(ii) Insufficient bearing lubrication (B1),
(iii) Severely insufficient bearing lubrication (B2),
(iv) Cracked bearing outer ring (B3),
(v) Voltage imbalance (V), and
(vi) Broken rotor bar (R).

Measurements were conducted at three operating speeds (30 Hz, 40 Hz, and 50 Hz) under both loaded and unloaded conditions, covering all combinations of fault type, speed, and load (36 operating scenarios in total). For each fault class, data corresponding to the six speed–load combinations are provided, and each recording is available in both .mat and .csv formats. As a result, each class folder contains 12 files, consisting of paired .mat and .csv files for the same measurement.

File names follow a standardized convention: Class_Load_SpeedHz, where the load condition is indicated by “1” (loaded) or “0” (unloaded). This organization facilitates straightforward access to the data and enables seamless integration into signal processing, machine learning, and statistical analysis workflows.

The dataset is intended for researchers working on vibration-based fault diagnosis, signal processing, and machine learning applications in electrical machines. By relying solely on smartphone inertial sensors, it offers a practical and cost-effective alternative to conventional industrial accelerometer-based datasets, enabling benchmarking, comparative studies, and the development of accessible motor condition monitoring solutions.
DOI: 10.17632/rs4vz8n3t5.1
title: A smartphone-based vibration dataset for induction motor fault diagnosis under different speed and load conditions

description: This dataset provides smartphone-based vibration measurements collected from a three-phase squirrel-cage induction motor operating under multiple fault conditions, rotational speeds, and load states. The primary objective of the dataset is to support research on low-cost and accessible condition monitoring and fault diagnosis of induction motors using inertial sensors embedded in consumer-grade smartphones.

Vibration signals were acquired using an iPhone 15 Pro Max equipped with a tri-axial accelerometer, with data recorded via the Sensor Play mobile application. Data were recorded along three raw acceleration axes (gx, gy, gz) as well as gravity-compensated linear acceleration components (guserx, gusery, guserz). All signals were sampled at 100 Hz, and each measurement corresponds to a continuous recording of 15 minutes. The smartphone was firmly attached to the motor housing using double-sided adhesive tape to ensure consistent coupling during data acquisition.

The dataset includes six motor health conditions:
(i) Healthy operation (H),
(ii) Insufficient bearing lubrication (B1),
(iii) Severely insufficient bearing lubrication (B2),
(iv) Cracked bearing outer ring (B3),
(v) Voltage imbalance (V), and
(vi) Broken rotor bar (R).

Measurements were conducted at three operating speeds (30 Hz, 40 Hz, and 50 Hz) under both loaded and unloaded conditions, covering all combinations of fault type, speed, and load (36 operating scenarios in total). For each fault class, data corresponding to the six speed–load combinations are provided, and each recording is available in both .mat and .csv formats. As a result, each class folder contains 12 files, consisting of paired .mat and .csv files for the same measurement.

File names follow a standardized convention: Class_Load_SpeedHz, where the load condition is indicated by “1” (loaded) or “0” (unloaded). This organization facilitates straightforward access to the data and enables seamless integration into signal processing, machine learning, and statistical analysis workflows.

The dataset is intended for researchers working on vibration-based fault diagnosis, signal processing, and machine learning applications in electrical machines. By relying solely on smartphone inertial sensors, it offers a practical and cost-effective alternative to conventional industrial accelerometer-based datasets, enabling benchmarking, comparative studies, and the development of accessible motor condition monitoring solutions.
DOI: 10.17632/5t3zbrn6xg
title: Biomarker-derived insights into hydrocarbon sourcing and intra-reservoir connectivity of the Triassic TAGI system, El Borma Field, southern Tunisia

description: This dataset contains the numerical data underlying all figures and tables in the manuscript entitled "Biomarker-derived insights into hydrocarbon sourcing and intra-reservoir connectivity of the Triassic TAGI system, El Borma Field, southern Tunisia"
DOI: 10.17632/5t3zbrn6xg.1
title: Biomarker-derived insights into hydrocarbon sourcing and intra-reservoir connectivity of the Triassic TAGI system, El Borma Field, southern Tunisia

description: This dataset contains the numerical data underlying all figures and tables in the manuscript entitled "Biomarker-derived insights into hydrocarbon sourcing and intra-reservoir connectivity of the Triassic TAGI system, El Borma Field, southern Tunisia"
DOI: 10.3886/e242265
title: Benue State University, Makurdi 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Benue State University, Makurdi 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.17632/nv729tbdgz.1
title: BigFlow-NIDS

description: BigFlow-NIDS, a large-scale, NetFlow-based dataset and accompanying analysis pipeline designed for intrusion-detection research in big-data environments. BigFlow-NIDS was created by merging four major benchmark NetFlow datasets (NF-UNSW-NB15-v3, NF-ToN-IoT-v3, NF-BoT-IoT-v3, NF-CSE-CIC-IDS2018-v3) using an Apache Spark preprocessing pipeline that performs deduplication, missing-value handling, label encoding, and feature harmonization. The final release contains 66,935,021 flows, 55 flow attributes, and 34 fine-grained attack categories, available in both CSV and Parquet formats to support scalable ML and streaming analyses.
DOI: 10.17632/cfm5kypbf7
title: Quantitative characterization of miasmatic states in homeopathy by multi-method research: Insights into diagnostic complexity and emergence of the continuous spectral model of miasmatic fingerprints

description: This study empirically validates chronic miasms in homeopathy as continuous spectral states rather than discrete categories, using the Systematic Miasm Assessment Tool (SMAT) on 931 chronic patients to reveal 74.7% mixed constitutions via Fuzzy C-Means clustering and weak physician-score alignment (η = 0.066-0.350). Soft-clustering (GMM/GoM) confirmed hybrid transitions, while hard partitioning failed (silhouette width 0.239), with sycosis/syphilis quantitative loads strongly predicting ORIDL success (OR = 1.41-2.21; 64.4% responders) over categorical diagnoses (κ = 0.161). SMAT enables precision fingerprinting akin to genomic/epigenetic continua, advancing prognosis and prescribing; multicenter studies are needed.