cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gp82j
title: CCDC 2284547: Experimental Crystal Structure Determination

description: Related Article: Adrian Sytniczuk, Filip Struzik, Karol Grela, Anna Kajetanowicz|2023|Chemical Science|14|10744|doi:10.1039/D3SC03849A
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.cc2gphc1
title: CCDC 2284773: Experimental Crystal Structure Determination

description: Related Article: Pauline Schiltz, Nicolas Casaretto, Sophie Bourcier, Audrey Auffrant, Corinne Gosmini|2023|Dalton Trans.|52|14859|doi:10.1039/D3DT02441E
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.cc2gq5w8
title: CCDC 2285440: Experimental Crystal Structure Determination

description: Related Article: Xin Guo, Shu-mei Cao, Xing Liu, Chunmei Huang, Jian Zhou|2023|Dalton Trans.|52|14297|doi:10.1039/D3DT02492J
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: 10.6084/m9.figshare.22354432
title: Early-life environmental effects on mitochondrial aerobic metabolism: a brood size manipulation in wild great tits

description: Original datasets for a brood size manipulation experiment. Data includes growth metrics, blood cell respirometry measurements and real-time qPCR measurements of mitochondrial density.
DOI: 10.6084/m9.figshare.22354432.v1
title: Early-life environmental effects on mitochondrial aerobic metabolism: a brood size manipulation in wild great tits

description: Original datasets for a brood size manipulation experiment. Data includes growth metrics, blood cell respirometry measurements and real-time qPCR measurements of mitochondrial density.
DOI: 10.6084/m9.figshare.24459748
title: Untitled Item

description: This is a Dataset of Algerian Famous Faces (AFFD), it contains 477 classes( person) and around 200 faces by classes tagged by its names.It will be used with a face recognition system for the Algerian TV canals to recognize characters.
DOI: 10.6084/m9.figshare.24459748.v1
title: Untitled Item

description: This is a Dataset of Algerian Famous Faces (AFFD), it contains 477 classes( person) and around 200 faces by classes tagged by its names.It will be used with a face recognition system for the Algerian TV canals to recognize characters.
DOI: 10.5281/zenodo.10054575
title: Potential vorticity and wind from ERA5 at several isentropic surfaces

description: This datasets collects winds (u and v components) and potential vorticity from ERA5 at four isentropic surfaces: 475, 600, 700 and 800 K. Data are available daily and monthly. Potential vorticity and modified potential vorticity are stored.
DOI: 10.5281/zenodo.10054574
title: Potential vorticity and wind from ERA5 at several isentropic surfaces

description: This datasets collects winds (u and v components) and potential vorticity from ERA5 at four isentropic surfaces: 475, 600, 700 and 800 K. Data are available daily and monthly. Potential vorticity and modified potential vorticity are stored.
DOI: 10.5281/zenodo.10054812
title: Phasor Identifier Test Dataset

description: The folder contains three sub-folders used in the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli. The data serve for specific examples on Phasor-FLIM customary analysis within the framework of the Python Notebook ran in Google Colab, extensively documented and available at: https://github.com/Mariochem92/PhasorIdentifier
Here you can find precompiled versions for the specific analysis of cellular and encapsulated drug analysis:

PhasorIdentifier_v1_0_3_Cellular_data.ipynb
PhasorIdentifier_v1_0_3_encapsulated_drug(ref format).ipynb
 
Those both work for any other analysis as well as a version of the code which is on ongoing development but more dispersive for newcomers, called PhasorIdentifier_v1_0_3_AllFunctionalities.ipynb
 
All the data was acquired at laser repetition frequency 80 MHz
 
Evolution of phasor-FLIM signals (DFD-FLIM acquisition)
Folders " Encapsulated irinotecan " and "Irinotecan and metabolite SN-38 at different pH" house files in .R64 format. Within, you'll find encapsulated irinotecan, irinotecan and metabolite SN-38 samples files. In this experiments irinotecan is encapsulated in the commercial nano formulation Onivyde in saline solution.
This data is represented in Fig 3 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.
 
Cellular Metabolism-Morphological and statistical analysis (DFD-FLIM acquisition)
The folder " INS1E cytokine stimulation and control " houses files in .R64 format. Within, you'll find INS1E control and INS1E cytokines, which respectively stand for INS1E samples and INS1E samples upon cytokines administration.
This data is represented in Fig 4 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.
 
Intensity and molar Fractions (TD-FLIM acquisition converted via Fourier Transform)
The folder "Doxoves Storage Files" houses a subset of Doxoves files in .ref format. Within, you'll find Doxoves4 and Doxoves37, which respectively stand for Doxoves samples stored at 4°C and 37°C. The numerical value following "Doxoves4/Doxoves37" indicates the duration of storage in days.
This data is represented in Fig 5 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.
DOI: 10.5281/zenodo.10054811
title: Phasor Identifier Test Dataset

description: The folder contains three sub-folders used in the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli. The data serve for specific examples on Phasor-FLIM customary analysis within the framework of the Python Notebook ran in Google Colab, extensively documented and available at: https://github.com/Mariochem92/PhasorIdentifier
Here you can find precompiled versions for the specific analysis of cellular and encapsulated drug analysis:

PhasorIdentifier_v1_0_3_Cellular_data.ipynb
PhasorIdentifier_v1_0_3_encapsulated_drug(ref format).ipynb
 
Those both work for any other analysis as well as a version of the code which is on ongoing development but more dispersive for newcomers, called PhasorIdentifier_v1_0_3_AllFunctionalities.ipynb
 
All the data was acquired at laser repetition frequency 80 MHz
 
Evolution of phasor-FLIM signals (DFD-FLIM acquisition)
Folders " Encapsulated irinotecan " and "Irinotecan and metabolite SN-38 at different pH" house files in .R64 format. Within, you'll find encapsulated irinotecan, irinotecan and metabolite SN-38 samples files. In this experiments irinotecan is encapsulated in the commercial nano formulation Onivyde in saline solution.
This data is represented in Fig 3 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.
 
Cellular Metabolism-Morphological and statistical analysis (DFD-FLIM acquisition)
The folder " INS1E cytokine stimulation and control " houses files in .R64 format. Within, you'll find INS1E control and INS1E cytokines, which respectively stand for INS1E samples and INS1E samples upon cytokines administration.
This data is represented in Fig 4 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.
 
Intensity and molar Fractions (TD-FLIM acquisition converted via Fourier Transform)
The folder "Doxoves Storage Files" houses a subset of Doxoves files in .ref format. Within, you'll find Doxoves4 and Doxoves37, which respectively stand for Doxoves samples stored at 4°C and 37°C. The numerical value following "Doxoves4/Doxoves37" indicates the duration of storage in days.
This data is represented in Fig 5 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.
DOI: 10.5281/zenodo.10054817
title: Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events

description: This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".
description: This research was supported by the Dutch Research Council (NWO) through Vici research program 'ARGO' with project
number 639.043.815 and through CORTEX (NWA.1160.18.316), under the research programme NWA-ORC.
DOI: 10.5281/zenodo.7472541
title: Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events

description: This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".
description: This research was supported by the Dutch Research Council (NWO) through Vici research program 'ARGO' with project
number 639.043.815 and through CORTEX (NWA.1160.18.316), under the research programme NWA-ORC.
DOI: 10.5281/zenodo.7472542
title: Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events

description: This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".
description: This research was supported by the Dutch Research Council (NWO) through Vici research program 'ARGO' with project number 639.043.815 and through CORTEX (NWA.1160.18.316), under the research programme NWA-ORC.
DOI: 10.5281/zenodo.10048158
title: Spatially averaged metocean data at Utsira Nord (UN) and Sørlige Nordsjø II (SN2) with NORA3 (1982-2022)

description: Overview
This dataset provides spatially averaged metocean data at Utsira Nord (UN) and Sørlige Nordsjø II (SN2) offshore site. The data span from 1982 to 2022 with a temporal resolution of 1 h and are formatted in NetCDF4. The data are valuable for a range of applications including, but not limited to, offshore engineering, marine renewable energy, climate studies, and environmental monitoring. The mean wind speed and mean direction data are provided at eight altitudes from 10 m to 750 m above sea level.
Data Description
The dataset is organised into NetCDF files with the following variables:
Dataset Variables and Dimensions
Time-Dependent Variables
Wind Direction Variables (Dir_median, Dir_q1, Dir_q99, Dir_q25, Dir_q75): Represent the mean wind direction at different percentiles, and are measured in degrees.
Wind Speed Variables (U_median, U_q1, U_q99, U_q25, U_q75): Indicate the mean wind speed at different percentiles, and are measured in metres per second (m/s).
Wave Height Variables (hs_median, hs_q1, hs_q99, hs_q25, hs_q75): Denote the significant wave height at different percentiles, and are measured in metres (m).
Wave Period Variables (tp_median, tp_q1, tp_q99, tp_q25, tp_q75): Capture the peak wave period at various percentiles, and are measured in seconds (s).
Friction Velocity Variables (u_star_median, u_star_q1, u_star_q99, u_star_q25, u_star_q75): Represent the friction velocity at different percentiles, and are measured in metres per second (m/s).
Wave Heading Variables (wd_median, wd_q1, wd_q99, wd_q25, wd_q75): Indicate the wave heading at different percentiles, and are measured in degrees.
Static Variables

Height Variable (z): Specifies the height above the surface, measured in metres (m). The dataset includes 8 levels: [10, 20, 50, 100, 150, 250, 500, 750].
Time Variable

Time Variable (time): Represents time in hours since 1970-01-01 00:00:00.
Data Dimensions

Dimensions:

time: 359400
z: 8
Data Types

The primary data type for these variables is double.
Usage
The NetCDF files can be accessed and manipulated using various programming languages that have NetCDF libraries, such as Python, MATLAB, R, and others. The dataset is suitable for both academic research and industrial applications.
DOI: 10.5281/zenodo.10048159
title: Spatially averaged metocean data at Utsira Nord (UN) and Sørlige Nordsjø II (SN2) with NORA3 (1982-2022)

description: Overview
This dataset provides spatially averaged metocean data at Utsira Nord (UN) and Sørlige Nordsjø II (SN2) offshore site. The data span from 1982 to 2022 with a temporal resolution of 1 h and are formatted in NetCDF4. The data are valuable for a range of applications including, but not limited to, offshore engineering, marine renewable energy, climate studies, and environmental monitoring. The mean wind speed and mean direction data are provided at eight altitudes from 10 m to 750 m above sea level.
Data Description
The dataset is organised into NetCDF files with the following variables:
Dataset Variables and Dimensions
Time-Dependent Variables
Wind Direction Variables (Dir_median, Dir_q1, Dir_q99, Dir_q25, Dir_q75): Represent the mean wind direction at different percentiles, and are measured in degrees.
Wind Speed Variables (U_median, U_q1, U_q99, U_q25, U_q75): Indicate the mean wind speed at different percentiles, and are measured in metres per second (m/s).
Wave Height Variables (hs_median, hs_q1, hs_q99, hs_q25, hs_q75): Denote the significant wave height at different percentiles, and are measured in metres (m).
Wave Period Variables (tp_median, tp_q1, tp_q99, tp_q25, tp_q75): Capture the peak wave period at various percentiles, and are measured in seconds (s).
Friction Velocity Variables (u_star_median, u_star_q1, u_star_q99, u_star_q25, u_star_q75): Represent the friction velocity at different percentiles, and are measured in metres per second (m/s).
Wave Heading Variables (wd_median, wd_q1, wd_q99, wd_q25, wd_q75): Indicate the wave heading at different percentiles, and are measured in degrees.
Static Variables

Height Variable (z): Specifies the height above the surface, measured in metres (m). The dataset includes 8 levels: [10, 20, 50, 100, 150, 250, 500, 750].
Time Variable

Time Variable (time): Represents time in hours since 1970-01-01 00:00:00.
Data Dimensions

Dimensions:

time: 359400
z: 8
Data Types

The primary data type for these variables is double.
Usage
The NetCDF files can be accessed and manipulated using various programming languages that have NetCDF libraries, such as Python, MATLAB, R, and others. The dataset is suitable for both academic research and industrial applications.
DOI: 10.5281/zenodo.10054768
title: MinION seq2
DOI: 10.5281/zenodo.10054769
title: MinION seq2
DOI: 10.6084/m9.figshare.24174576
title: Supplementary Material for: Selected β-glucans act as immune-training agents by improving anti-mycobacterial activity in human macrophages - a pilot study.

description: Epigenetic reprogramming of innate immune cells by β-glucan in a process called trained immunity, leads to an enhanced host response to a secondary infection. β-glucans are structural components of plants, algae, fungi and bacteria and thus recognized as non-self by human macrophages. We selected the β-glucans curdlan from Alcaligenes faecalis, WGP dispersible from Saccharomyces cerevisiae, and β-glucan-rich culture supernatant of Alternaria and investigated whether they could produce trained immunity effects leading to an increased control of virulent Mycobacterium tuberculosis. We observed a significant M. tuberculosis growth-reduction in macrophages trained with curdlan and Alternaria, which also correlated with increased IL-6 and IL-1β release. WGP dispersible-trained macrophages were stratified into ‘non responders’ and ‘responders’, according to their ability to control M. tuberculosis, with ‘responders’ producing higher IL-6 levels. The addition of neutrophils to infected macrophage cultures further enhanced macrophage control of virulent M. tuberculosis, but not in a stimuli-dependent manner. Pathway enrichment analysis of DNA methylome data also highlighted hypomethylation of genes in pathways associated with signaling and cellular reorganization and motility, and ‘responders’ to WGP-training were enriched in the interferon-gamma signaling pathway. This study adds evidence that certain β-glucans show promise as immune training agents.
DOI: 10.5281/zenodo.10048147
title: TGF beta
DOI: 10.5281/zenodo.10048146
title: TGF beta
DOI: 10.6084/m9.figshare.24321307
title: Supplementary Material for: Downstaging and Pathological Complete Response of Locally Recurrent Sarcomatoid Renal Cell Carcinoma under Pembrolizumab and Lenvatinib: A Case Report and Review of Literature

description: The advent of immune checkpoint inhibition opened new perspectives for patients with recurrent or metastasized renal cell carcinoma. In case of recurrent disease, surgical resection remains the most promising therapeutic option. Surgical resection is associated with improved overall survival and demonstrated curative potential given complete resection of metastases can be performed. This report presents the case of a patient with local recurrence of dedifferentiated sarcomatoid renal cell carcinoma approximately one year after initial open lumbar nephrectomy. After initial evaluation, surgical removal was deemed infeasible and an induction therapy with pembrolizumab and lenvatinib was initiated. After three months, corresponding to 5 cycles of pembrolizumab, the tumor showed a partial response on imaging control and was successfully resected en-bloc. Histopathological examination of the specimen revealed no evidence of viable neoplastic cells. This is the first report describing a complete pathological response of a locally recurrent dedifferentiated sarcomatoid renal cell carcinoma after treatment with pembrolizumab and lenvatinib. Overall, the combination therapy was well tolerated with a maximum Common Terminology Criteria for Adverse Events Level of two. These findings underline the potential of multimodal therapeutic strategies for recurrent renal cell carcinoma, such as induction therapies to downstage initially non-resectable masses and highlight the need for prospective studies to allow for evidence-based treatment plans.
DOI: 10.6084/m9.figshare.24460765
title: 20231031_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24460765.v1
title: 20231031_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.10054847
title: AFM data of ice clusters on Cu(111) and Au(111) in paper "Structure discovery in Atomic Force Microscopy imaging of ice"

description: Frequency shift CO-tip atomic force microscopy data of small ice clusters on Cu(111) and Au(111) surfaces as they appear in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".
The data are saved in a compressed .tar.gz archive. The unpacked archive contains each experiment as a Numpy .npz file. Each file contains the measurement data as a 3D array in the key 'data' and the physical extent of the scan region in the x and y directions in Ånströms in the keys 'lengthX' and 'lengthY'.
DOI: 10.5281/zenodo.10054846
title: AFM data of ice clusters on Cu(111) and Au(111) in paper "Structure discovery in Atomic Force Microscopy imaging of ice"

description: Frequency shift CO-tip atomic force microscopy data of small ice clusters on Cu(111) and Au(111) surfaces as they appear in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".
The data are saved in a compressed .tar.gz archive. The unpacked archive contains each experiment as a Numpy .npz file. Each file contains the measurement data as a 3D array in the key 'data' and the physical extent of the scan region in the x and y directions in Ånströms in the keys 'lengthX' and 'lengthY'.
DOI: 10.6084/m9.figshare.24460114.v2
title: lunar_surface_chemistry_maps

description: The data files depict the abundances of SiO2, TiO2 and MgO observed by the KAGUYA multiband imager and TiO2, FeO and Al2O3 of the young mare basalts unit. The data files are in the format of .tif and can be opend by the software ArcGIS.
DOI: 10.21227/9ywp-1h32
title: LSD4WSD VX: An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling - Full Analysis Version

description: Learning SAR Dataset for Wet Snow Detection - Full Analysis Version. The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample. It consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the Crocus physical model.An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.
DOI: 10.6084/m9.figshare.16910170.v1
title: Data and Code for "Evolutionary dynamics of mutants that modify population structure"

description: Data and Code for "Evolutionary dynamics of mutants that modify population structure": Python scripts used to compute the data and Mathematica notebooks used to produce the plots.
DOI: 10.21227/ej9p-rw18
title: MATLAB Files: (Paper) Data-Driven Saturated State Feedback Design for Polynomial Systems Using Noisy Data

description: The auploaded .ZIP file contains the MATLAB codes used in Examples 1 and 2 of the following paper, which the authors have submitted to an IEEE Journal: Data-Driven Saturated State Feedback Design for Polynomial Systems Using Noisy Data. This is the abstract of the paper: "In this note, the problem of data-driven saturated state feedback design for polynomial nonlinear systems is solved by means of a sum-of-squares (SOS) approach. This new strategy combines recent results in dissipativity theory and data-driven feedback control using noisy input-state data. SOS optimization is also employed in this work to deliver an estimate of the closed-loop domain of attraction under saturated feedback. Numerical examples allow the reader to verify the usefulness of the proposed strategy, which is the first in literature to provide a data-driven and dissipativity-based approach for dealing with the problem of input saturation for continuous-time polynomial systems".
DOI: 10.15454/foifcn/cazmmc
title: SSRseq_PannelSummary_PGTB.tab

description: :unav
DOI: 10.15454/foifcn/sfjix5
title: SSRseq_PannelSummary_PGTB.tab

description: :unav
DOI: 10.57745/mtum0t
title: SSRseq_PannelSummary_PGTB-1.tab

description: :unav
DOI: 10.57745/rw8dbz
title: SSRseq_PannelSummary_PGTB.tab

description: :unav