cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29136782
title: Supplementary Material for: Different course of nonpigmented skin cancers in patients treated with immunosuppression after organ transplantation - a case series.

description: Introduction
Only in 2023 solid organs were transplanted in 1400 patients in Poland. Nonmelanoma skin cancer such as squamous cell carcinoma (SCC) or basal cell carcinoma (BCC) is the most common malignancy in solid organ transplant recipients (SOTR).
The aim of the study is to present cases and discuss the possible course of skin cancer in patients after organ transplantation.
Case presentation
The study involved 3 patients after organ transplantation treated for skin cancer  in the Department of Plastic Surgery of the University of Gdańsk in the years 2020-2023.
Each patient is an example of a different course of the disease. In the case of patients treated with immunosuppression, a more aggressive course of the cancer should be considered, which may take various forms. One of the variants that we are dealing with in the first patient is the occurrence of multiple lesions, the co-occurrence of SCC and BCC and frequent local recurrences. In the case of the second patient, we are dealing with a very aggressive course despite the early initiation of appropriate treatment. Another example of a different course is an atypical clinical picture that makes it difficult to make the correct diagnosis, which we observe in the third patient.
Conclusions
Our study demonstrates that due to the much higher morbidity, aggressive, rapid progression of the skin cancer disease and unfavorable prognosis, population after transplantation requires a special oncological approach.
DOI: 10.6084/m9.figshare.29136782.v1
title: Supplementary Material for: Different course of nonpigmented skin cancers in patients treated with immunosuppression after organ transplantation - a case series.

description: Introduction
Only in 2023 solid organs were transplanted in 1400 patients in Poland. Nonmelanoma skin cancer such as squamous cell carcinoma (SCC) or basal cell carcinoma (BCC) is the most common malignancy in solid organ transplant recipients (SOTR).
The aim of the study is to present cases and discuss the possible course of skin cancer in patients after organ transplantation.
Case presentation
The study involved 3 patients after organ transplantation treated for skin cancer  in the Department of Plastic Surgery of the University of Gdańsk in the years 2020-2023.
Each patient is an example of a different course of the disease. In the case of patients treated with immunosuppression, a more aggressive course of the cancer should be considered, which may take various forms. One of the variants that we are dealing with in the first patient is the occurrence of multiple lesions, the co-occurrence of SCC and BCC and frequent local recurrences. In the case of the second patient, we are dealing with a very aggressive course despite the early initiation of appropriate treatment. Another example of a different course is an atypical clinical picture that makes it difficult to make the correct diagnosis, which we observe in the third patient.
Conclusions
Our study demonstrates that due to the much higher morbidity, aggressive, rapid progression of the skin cancer disease and unfavorable prognosis, population after transplantation requires a special oncological approach.
DOI: 10.57701/suf.rtzn.2001-2022.em.1-0
title: Zeitreihe Versichertenrentenzugang - Erwerbsminderung und Diagnosen 2001 - 2022
DOI: 10.5281/zenodo.15497199
title: Silicon spectrum for calibration
DOI: 10.5281/zenodo.15497200
title: Silicon spectrum for calibration
DOI: 10.5281/zenodo.15497236
title: Calculations on Maxwell-Wagner effect in 5PPE - Kapton and 5PPE - PEEK interfaces
DOI: 10.5281/zenodo.15497237
title: Calculations on Maxwell-Wagner effect in 5PPE - Kapton and 5PPE - PEEK interfaces
DOI: 10.57701/suf.rtbn.2020.1-0
title: Versichertenrentenbestand 2020
DOI: 10.15468/dl.k9rk8g
title: Occurrence Download

description: A dataset containing 40237 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "Elevation 915.0-9999.0m",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 40237 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015563-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10017718
title: Metal Arc Welding

description: Predictive Quality Arc Welding Dataset
The dataset comprises various current and voltage time series. Both currents and voltages are synchronously sampled at a frequency 100 kHz, with a maximum permissible error of 0.5%.
Raw data
Column Name   Description
------------  ----------------------------------------------
experiment    ID of the experiment run
welding_run   ID welding in one experiment
cycle         ID cycle in one welding run
I             Current
V             Voltage
quality       Quality label (0: bad weld quality | 1: good weld quality | -1: no label)
 
Preprocessed data
 
Column Name   Description
------------  -------------------------------------------------------------
experiment    ID of the experiment run
welding_run   ID welding in one experiment
label         Quality label (0: bad weld quality | 1: good weld quality | -1: no label)
V_0           Voltage at the beginning of the cycle (t_0)
...           Voltage from (t_1) to (t_198)
V_199         Voltage at the end of the cycle
I_0           Current at the beginning of the cycle (t_0)
...           Current from (t_1) to (t_198)
I_199         Current at the end of the cycle
DOI: 10.5281/zenodo.15101072
title: Metal Arc Welding

description: Predictive Quality Arc Welding Dataset

The dataset comprises various current and voltage time series. Both currents and voltages are synchronously sampled at a frequency 100 kHz, with a maximum permissible error of 0.5%.

 

Preprocessed Data

Column Name   Description

------------  -------------------------------------------------------------

labels         Quality label (0: bad weld quality | 1: good weld quality | -1: no label)

exp_ids       ID of the experiment run

V_000           Voltage at the beginning of the cycle (t_0)

...           Voltage from (t_1) to (t_198)

V_199         Voltage at the end of the cycle

I_000           Current at the beginning of the cycle (t_0)

...           Current from (t_1) to (t_198)

I_199         Current at the end of the cycle

Code Sample Reading the Data

import numpy as np
import pandas as pd  

def convert_to_np(data: pd.DataFrame) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Convert DataFrame to numpy arrays, separating labels, experiment IDs, and features.

    Args:
        data (pd.DataFrame): Input DataFrame containing 'labels', 'exp_ids', and feature columns.

    Returns:
        tuple: A tuple containing:
            - labels (np.ndarray): Array of labels
            - exp_ids (np.ndarray): Array of experiment IDs
            - data (np.ndarray): Combined array of current and voltage features
    """
    logging.info(f"Converting data to numpy array")
    labels, exp_ids = data["labels"].values, data["exp_ids"].values
    data = data.drop(columns=["labels", "exp_ids"])
    
    cols_v = data.columns[data.columns.str.startswith("V")]
    cols_i = data.columns[data.columns.str.startswith("I")]
    
    current_data = data[cols_i].values
    voltage_data = data[cols_v].values

    data = np.stack([current_data, voltage_data], axis=2)

    return labels, exp_ids, data

data_path = ""
data = pd.read_csv(data_path)
labels, exp_ids, data = convert_to_np(data)
DOI: 10.5281/zenodo.7023254
title: Metal Arc Welding

description: Contains sensor data from 32 welding procedures with different types of error patterns. The json file contains a list of objects with the following structure: <pre>{ "time": date and time in iso format with microseconds resolution "voltage": voltage measured at the welding electrodes "current": current measured at the welding electrodes }</pre>
DOI: 10.5281/zenodo.15497262
title: Metal Arc Welding

description: Predictive Quality Arc Welding Dataset

The dataset comprises various current and voltage time series. Both currents and voltages are synchronously sampled at a frequency 100 kHz, with a maximum permissible error of 0.5%.

 

Preprocessed Data

Column Name   Description

------------  -------------------------------------------------------------

labels         Quality label (0: bad weld quality | 1: good weld quality | -1: no label)

exp_ids       ID of the experiment run



welding_run_id : ID of the welding run


V_000           Voltage at the beginning of the cycle (t_0)

...           Voltage from (t_1) to (t_198)

V_199         Voltage at the end of the cycle

I_000           Current at the beginning of the cycle (t_0)

...           Current from (t_1) to (t_198)

I_199         Current at the end of the cycle

Code Sample Reading the Data

import numpy as np
import pandas as pd  

def convert_to_np(data: pd.DataFrame) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Convert DataFrame to numpy arrays, separating labels, experiment IDs, and features.

    Args:
        data (pd.DataFrame): Input DataFrame containing 'labels', 'exp_ids', and feature columns.

    Returns:
        tuple: A tuple containing:
            - labels (np.ndarray): Array of labels
            - exp_ids (np.ndarray): Array of experiment IDs
            - data (np.ndarray): Combined array of current and voltage features
    """
    logging.info(f"Converting data to numpy array")
    labels, exp_ids, welding_run_ids = data["labels"].values, data["exp_ids"].values, df["welding_run_id"].values 
    data = data.drop(columns=["labels", "exp_ids"])
    
    cols_v = data.columns[data.columns.str.startswith("V")]
    cols_i = data.columns[data.columns.str.startswith("I")]
    
    current_data = data[cols_i].values
    voltage_data = data[cols_v].values

    data = np.stack([current_data, voltage_data], axis=2)

    return labels, exp_ids, welding_run_ids, data

data_path = ""
data = pd.read_csv(data_path)
labels, exp_ids, welding_run_ids, data = convert_to_np(data)
DOI: 10.5281/zenodo.7023253
title: Metal Arc Welding

description: Predictive Quality Arc Welding Dataset

The dataset comprises various current and voltage time series. Both currents and voltages are synchronously sampled at a frequency 100 kHz, with a maximum permissible error of 0.5%.

 

Preprocessed Data

Column Name   Description

------------  -------------------------------------------------------------

labels         Quality label (0: bad weld quality | 1: good weld quality | -1: no label)

exp_ids       ID of the experiment run



welding_run_id : ID of the welding run


V_000           Voltage at the beginning of the cycle (t_0)

...           Voltage from (t_1) to (t_198)

V_199         Voltage at the end of the cycle

I_000           Current at the beginning of the cycle (t_0)

...           Current from (t_1) to (t_198)

I_199         Current at the end of the cycle

Code Sample Reading the Data

import numpy as np
import pandas as pd  

def convert_to_np(data: pd.DataFrame) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Convert DataFrame to numpy arrays, separating labels, experiment IDs, and features.

    Args:
        data (pd.DataFrame): Input DataFrame containing 'labels', 'exp_ids', and feature columns.

    Returns:
        tuple: A tuple containing:
            - labels (np.ndarray): Array of labels
            - exp_ids (np.ndarray): Array of experiment IDs
            - data (np.ndarray): Combined array of current and voltage features
    """
    logging.info(f"Converting data to numpy array")
    labels, exp_ids, welding_run_ids = data["labels"].values, data["exp_ids"].values, df["welding_run_id"].values 
    data = data.drop(columns=["labels", "exp_ids"])
    
    cols_v = data.columns[data.columns.str.startswith("V")]
    cols_i = data.columns[data.columns.str.startswith("I")]
    
    current_data = data[cols_i].values
    voltage_data = data[cols_v].values

    data = np.stack([current_data, voltage_data], axis=2)

    return labels, exp_ids, welding_run_ids, data

data_path = ""
data = pd.read_csv(data_path)
labels, exp_ids, welding_run_ids, data = convert_to_np(data)
DOI: 10.34740/kaggle/dsv/11922727
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.34740/kaggle/dsv/11808062
title: Similar System Data Set for Fault Diagnosis
DOI: 10.15468/dl.nktrgj
title: Occurrence Download

description: A dataset containing 6558 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bothriechis Peters, 1859"
 ]
}

 The dataset includes 6558 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015565-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6x2s5y
title: Occurrence Download

description: A dataset containing 827 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bothrocophias Gutberlet & Campbell, 2001"
 ]
}

 The dataset includes 827 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015566-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6w8h2
title: Occurrence Download

description: A dataset containing 90150 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((57.8032 19.40516,139.58432 19.8659,138.96999 63.25213,57.26567 63.4825,57.8032 19.40516))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Brassicaceae",
 "Year 1994-2025"
 ]
}

 The dataset includes 90150 records from 272 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015562-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57701/suf.rtzn.2020.1-0
title: Versichertenrentenzugang 2020
DOI: 10.15468/dl.8c82t2
title: Occurrence Download

description: A dataset containing 5391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Meles meles (Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((6.216967 45.590977, 10.741974 45.590977, 10.741974 47.791016, 6.216967 47.791016, 6.216967 45.590977))",
 "Year is greater than or equal to 1989",
 "Year is less than or equal to 1999"
 ]
}

 The dataset includes 5391 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015564-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57701/suf.rtbn.2019.1-0
title: Versichertenrentenbestand 2019
DOI: 10.57701/suf.rtzn.2001-2021.em.1-0
title: Zeitreihe Versichertenrentenzugang - Erwerbsminderung und Diagnosen 2001 - 2021
DOI: 10.15468/dl.msj4ty
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence)",
 "Country is China",
 "TaxonKey is Nomascus concolor (Harlan, 1826)",
 "Year 2005-2025"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015567-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57701/suf.rtwf.2001-2022.td.1-0
title: Zeitreihe Versichertenrentenwegfall wegen Todes 2001 - 2022
DOI: 10.5281/zenodo.10887651
title: Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model

description: Python scripts used for GMD paper
DOI: 10.5281/zenodo.10887652
title: Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model

description: Python scripts used for GMD paper
DOI: 10.5281/zenodo.10907278
title: Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model

description: Python scripts used for GMD paper
DOI: 10.5281/zenodo.15497274
title: Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model

description: Python scripts used for GMD paper
DOI: 10.6084/m9.figshare.28934069
title: A well-characterized mechanistic model for exploring known or hypothesized T cell mediated drug induced liver injury: current capabilities and challenges for future predictivity

description: Drug-induced liver injury (DILI) is an adverse event whose emergence can slow or halt drug development programs. Adaptive immune responses have been implicated for several DILI compounds, and drug-specific T cell responses have been characterized, but there are still many unknowns. We describe the extension of a quantitative systems toxicology (QST) model of DILI to include CD8<sup>+</sup> T cell-mediated DILI. To overcome deficits in quantitative data characterizing CD8<sup>+</sup> T cell-mediated DILI, a translational strategy leveraged a well-defined mouse ovalbumin (OVA) antigen model and adapted it to represent mouse amodiaquine (AQ)-specific CD8<sup>+</sup> T cell-mediated DILI, with further adaptations to represent human AQ-specific CD8<sup>+</sup> T cell-mediated DILI. DILIsym reproduced published data characterizing mouse OVA-specific CD8<sup>+</sup> T cell-mediated hepatotoxicity, mouse AQ-specific CD8<sup>+</sup> T cell-mediated DILI, and human AQ-specific CD8<sup>+</sup> T cell-mediated DILI. Development identified main drivers of the CD8<sup>+</sup> T cell response, as well as areas where <i>in vitro</i> assay data could inform the simulation of additional compounds. The DILIsym CD8<sup>+</sup> T cell sub-model is well-positioned for systematic testing to improve our understanding of CD8<sup>+</sup> T cell-mediated DILI. It is not yet predictive but indicates a promising direction to reduce DILI events in drug development.
DOI: 10.6084/m9.figshare.28934069.v1
title: A well-characterized mechanistic Model for exploring known or hypothesized T cell -mediated drug induced liver injury: current capabilities and challenges for future predictivity

description: Drug-induced liver injury (DILI) is an adverse event whose emergence can slow or halt drug development programs. Adaptive immune responses have been implicated for several DILI compounds, and drug-specific T cell responses have been characterized, but there are still many unknowns. We describe the extension of a quantitative systems toxicology (QST) model of DILI to include CD8<sup>+</sup> T cell-mediated DILI. To overcome deficits in quantitative data characterizing CD8<sup>+</sup> T cell-mediated DILI, a translational strategy leveraged a well-defined mouse ovalbumin (OVA) antigen model and adapted it to represent mouse amodiaquine (AQ)-specific CD8<sup>+</sup> T cell-mediated DILI, with further adaptations to represent human AQ-specific CD8<sup>+</sup> T cell-mediated DILI. DILIsym reproduced published data characterizing mouse OVA-specific CD8<sup>+</sup> T cell-mediated hepatotoxicity, mouse AQ-specific CD8<sup>+</sup> T cell-mediated DILI, and human AQ-specific CD8<sup>+</sup> T cell-mediated DILI. Development identified main drivers of the CD8<sup>+</sup> T cell response, as well as areas where <i>in vitro</i> assay data could inform the simulation of additional compounds. The DILIsym CD8<sup>+</sup> T cell sub-model is well-positioned for systematic testing to improve our understanding of CD8<sup>+</sup> T cell-mediated DILI. It is not yet predictive but indicates a promising direction to reduce DILI events in drug development.
DOI: 10.6084/m9.figshare.24630808
title: The ghost tumour revisited. Corticosteroids in primary central nervous system lymphoma: diagnostic, prognostic and therapeutic implications

description: The cytolytic effect of corticosteroids on primary central nervous system lymphoma (PCNSL) has established the clinical dogma of avoiding steroid therapy prior to surgery for diagnostic purposes. However, since steroids are very useful during the initial management of intracranial lesions with vasogenic oedema, it was our aim to determine whether they cause a drawback in the diagnosis and prognosis of PCNSL. A retrospective cohort study of patients diagnosed with PCNSL between 2000 and 2020 in our tertiary neurosurgical centre. Data on steroid administration, surgery type and complications, haematopathological findings and prognostic factors were compiled. A second cohort was used as a control group to compare the ratio of non-diagnostic biopsies; this series comprised patients who underwent stereotactic brain biopsy for any reason between 2019 and 2020. Forty patients with PCNSL were included in the study, of which 28 (70%) had received steroids before surgery. The use of steroids was more prevalent in patients with poorer performance status at diagnosis. No relevant differences were found in the diagnostic accuracy regardless of steroid exposure (93% under steroids vs 100% without steroids) or type of surgery performed. Furthermore, steroid withdrawal did not seem to augment the diagnostic ratio. The notable diagnostic delay was not influenced by the use of steroids. Novel imaging and surgical techniques might obviate the need to withhold corticosteroids from patients suffering from PCNSL prior to biopsy. Moreover, when steroids have been given, tapering them and delaying the surgery might not be justified. This could hold relevant therapeutic implications in the early clinical stages.
DOI: 10.6084/m9.figshare.24630808.v1
title: The ghost tumour revisited. Corticosteroids in primary central nervous system lymphoma: diagnostic, prognostic and therapeutic implications

description: The cytolytic effect of corticosteroids on primary central nervous system lymphoma (PCNSL) has established the clinical dogma of avoiding steroid therapy prior to surgery for diagnostic purposes. However, since steroids are very useful during the initial management of intracranial lesions with vasogenic oedema, it was our aim to determine whether they cause a drawback in the diagnosis and prognosis of PCNSL. A retrospective cohort study of patients diagnosed with PCNSL between 2000 and 2020 in our tertiary neurosurgical centre. Data on steroid administration, surgery type and complications, haematopathological findings and prognostic factors were compiled. A second cohort was used as a control group to compare the ratio of non-diagnostic biopsies; this series comprised patients who underwent stereotactic brain biopsy for any reason between 2019 and 2020. Forty patients with PCNSL were included in the study, of which 28 (70%) had received steroids before surgery. The use of steroids was more prevalent in patients with poorer performance status at diagnosis. No relevant differences were found in the diagnostic accuracy regardless of steroid exposure (93% under steroids vs 100% without steroids) or type of surgery performed. Furthermore, steroid withdrawal did not seem to augment the diagnostic ratio. The notable diagnostic delay was not influenced by the use of steroids. Novel imaging and surgical techniques might obviate the need to withhold corticosteroids from patients suffering from PCNSL prior to biopsy. Moreover, when steroids have been given, tapering them and delaying the surgery might not be justified. This could hold relevant therapeutic implications in the early clinical stages.