cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29329469.v6
title: <i>Toxoplasma gondii </i>effector GRA35 mediates neuronal damage <i>via</i> ER stress and mitochondria-associated apoptosis

description: Encephalitis resulting from acute reactivation of chronic <i>Toxoplasma gondii</i> infection in the central nervous system poses a significant mortality risk in immunodeficient individuals. However, the specific molecular mechanisms underlying this process remain elusive. We constructed the GRA35 gene knockout ME49 strain and compared the differences with wild type ME49 strain. We used the GST-pull down experiment to explore the mechanism of GRA35 promoting neuronal cell apoptosis. We used immunofluorescence, flow cytometry and CCK8 experiments to verify the pathway of GRA35 promoting neuronal cell apoptosis. Our study reveals that wild type ME49 strain promotes neuronal apoptosis in brain following chronic infection activation. Conversely, infection with the ME49<sup>Δ</sup><sup><em>gra35</em></sup> strain leads to a reduced apoptotic response in brain neurons. Furthermore, we demonstrate that GRA35 interacts with RTN1-c, thereby promoting mitochondrial pathway-mediated apoptosis in neurons. Additionally, GRA35 can trigger host cell ER stress-associated apoptosis through the PERK signaling pathway. GRA35 serves as a crucial virulence factor in the pathogenesis of Toxoplasmic encephalitis (TE), which offers potential new therapeutic target and theoretical insights for TE.
DOI: 10.6084/m9.figshare.29329469.v5
title: <i>Toxoplasma gondii </i><b>effector GRA35</b> <b>mediates neuronal damage </b><b><i>via</i></b><b> </b><b>ER stress and </b><b>mitochondria-associated apoptosis</b>

description: Encephalitis resulting from acute reactivation of chronic <i>Toxoplasma gondii</i> infection in the central nervous system poses a significant mortality risk in immunodeficient individuals. However, the specific molecular mechanisms underlying this process remain elusive. We constructed the GRA35 gene knockout ME49 strain and compared the differences with wild type ME49 strain. We used the GST-pull down experiment to explore the mechanism of GRA35 promoting neuronal cell apoptosis. We used immunofluorescence, flow cytometry and CCK8 experiments to verify the pathway of GRA35 promoting neuronal cell apoptosis. Our study reveals that wild type ME49 strain promotes neuronal apoptosis in brain following chronic infection activation. Conversely, infection with the ME49<sup>Δ</sup><sup><em>gra35</em></sup> strain leads to a reduced apoptotic response in brain neurons. Furthermore, we demonstrate that GRA35 interacts with RTN1-c, thereby promoting mitochondrial pathway-mediated apoptosis in neurons. Additionally, GRA35 can trigger host cell ER stress-associated apoptosis through the PERK signaling pathway. GRA35 serves as a crucial virulence factor in the pathogenesis of Toxoplasmic encephalitis (TE), which offers potential new therapeutic target and theoretical insights for TE.
DOI: 10.15468/dl.8wpmua
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cattleya walkeriana",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 61 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011184-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17727089
title: 3D VNWFET with Double Gate Stack (JL2) -Based Standard Cell Libraries

description: Vertical Nanowire Field Effect Transistors (VNWFET) are emerging as a promising technology to overcome the scaling limitations of conventional lateral devices. The Junction-less (JL) device with a 3D gate-all-around (GAA) architecture offers several key advantages: improved energy consumption, reduced footprint, enablement of the gate stacking approach to benefit designs with multiple transistors in series.

This dataset is considered as an extension of our previous work on the generation of standard cell library based on single gate (JL1) VNWFET devices. In this work, we consider different design approach for implementing the double gate stacking (JL2):



Mixed Configuration or Hybrid JL2: containing both single- and double-gate transistors (i.e. double-gate is only present in the network with two transistors in series)

JL2 variants employing two gate contacts per nanowire:



JL2 pure: two gates of each transistor are connected to one of the inputs

JL2 G1: the bottom gates are tied to ground (resp. Vdd) in p-type (resp. n-type) devices

JL2 G2: the top gates are tied to ground (resp. Vdd) in p-type (resp. n-type) devices




These three configurations correspond to the networks of the cells that do not contain series transistors (i.e. single gate in previous Hybrid design), whereas the networks with initially double gates have the same design in the three above approaches. (i.e. a single input at each gate).

This dataset includes the outcome of this flow presented by the liberty file of standard cell libraries based on 3D VNWFET devices while including the resistance and capacitance parasitic for the characterization of the cells. The files correspond to two threshold voltages: Regular (RVT) and Low (LVT) and composed of the following cells:



BUFX1_CStatic_JL1

INVX1_CStatic_JL1

NAND2X1_CStatic_JL1

NOR2X1_CStatic_JL1

XOR2X1_CStatic_JL1

Asynch_DFFX1_CStatic_JL1


where the formalism OPnXk_Style_Technology  indicates the Boolean operation OP, the number of inputs n and the drive strength k (k=1 corresponds to 4 NWs per device), as well as the logic design style  (i.e., Complementary static logic obtained by using n- and p-type VNWFET) and the technology variant used to implement the cells: Junction-Less Nanowire: (JL2_Hybrid, JL2_Pure, JL2_G1_0 and JL2_G2_0). In this data set, we also provide the physical layout (in GDSII format) of the logic cells present in the generated liberty files.
DOI: 10.5281/zenodo.17727090
title: 3D VNWFET with Double Gate Stack (JL2) -Based Standard Cell Libraries

description: Vertical Nanowire Field Effect Transistors (VNWFET) are emerging as a promising technology to overcome the scaling limitations of conventional lateral devices. The Junction-less (JL) device with a 3D gate-all-around (GAA) architecture offers several key advantages: improved energy consumption, reduced footprint, enablement of the gate stacking approach to benefit designs with multiple transistors in series.

This dataset is considered as an extension of our previous work on the generation of standard cell library based on single gate (JL1) VNWFET devices. In this work, we consider different design approach for implementing the double gate stacking (JL2):



Mixed Configuration or Hybrid JL2: containing both single- and double-gate transistors (i.e. double-gate is only present in the network with two transistors in series)

JL2 variants employing two gate contacts per nanowire:



JL2 pure: two gates of each transistor are connected to one of the inputs

JL2 G1: the bottom gates are tied to ground (resp. Vdd) in p-type (resp. n-type) devices

JL2 G2: the top gates are tied to ground (resp. Vdd) in p-type (resp. n-type) devices




These three configurations correspond to the networks of the cells that do not contain series transistors (i.e. single gate in previous Hybrid design), whereas the networks with initially double gates have the same design in the three above approaches. (i.e. a single input at each gate).

This dataset includes the outcome of this flow presented by the liberty file of standard cell libraries based on 3D VNWFET devices while including the resistance and capacitance parasitic for the characterization of the cells. The files correspond to two threshold voltages: Regular (RVT) and Low (LVT) and composed of the following cells:



BUFX1_CStatic_JL1

INVX1_CStatic_JL1

NAND2X1_CStatic_JL1

NOR2X1_CStatic_JL1

XOR2X1_CStatic_JL1

Asynch_DFFX1_CStatic_JL1


where the formalism OPnXk_Style_Technology  indicates the Boolean operation OP, the number of inputs n and the drive strength k (k=1 corresponds to 4 NWs per device), as well as the logic design style  (i.e., Complementary static logic obtained by using n- and p-type VNWFET) and the technology variant used to implement the cells: Junction-Less Nanowire: (JL2_Hybrid, JL2_Pure, JL2_G1_0 and JL2_G2_0). In this data set, we also provide the physical layout (in GDSII format) of the logic cells present in the generated liberty files.
DOI: 10.5281/zenodo.18526765
title: WikiLingDiv

description: This dataset is a resource developed to support research on digital linguistic diversity. Quantitative research in the field is limited by the lack of openly available spatiotemporal data to quantify digital language use. While there are multiple dimensions of digital linguistic diversity, such as production and presence, this dataset focuses specifically on knowledge retrieval proxied by page views across different language editions of Wikipedia. As such, the dataset provides a unique opportunity to study the digital consumption of language.

To construct the dataset, the Wikimedia API was queried for the number of page views to each language edition for a given year and country from 2015 to 2024. 

The data was enriched with ISO639-3 language codes and structured and merged into a single CSV: country_year_lang_views.csv. Based on this, we precompute three commonly used measures of diversity: Richness, Exponent-Shannon, and Inverse-Simpson for each country and year, yielding the timeseries diversity_measures.csv. Furthermore, additional data from Glottolog and rnaturalearthdata concerning the countries and languages included in the dataset are provided in country_data.csv and language_data.csv to facilitate analysis. 

The data can be explored interactively at: https://f39e09-hannes-essfors.shinyapps.io/wikilingdiv_dashboard/
DOI: 10.5281/zenodo.18526766
title: WikiLingDiv

description: This dataset is a resource developed to support research on digital linguistic diversity. Quantitative research in the field is limited by the lack of openly available spatiotemporal data to quantify digital language use. While there are multiple dimensions of digital linguistic diversity, such as production and presence, this dataset focuses specifically on knowledge retrieval proxied by page views across different language editions of Wikipedia. As such, the dataset provides a unique opportunity to study the digital consumption of language.

To construct the dataset, the Wikimedia API was queried for the number of page views to each language edition for a given year and country from 2015 to 2024. 

The data was enriched with ISO639-3 language codes and structured and merged into a single CSV: country_year_lang_views.csv. Based on this, we precompute three commonly used measures of diversity: Richness, Exponent-Shannon, and Inverse-Simpson for each country and year, yielding the timeseries diversity_measures.csv. Furthermore, additional data from Glottolog and rnaturalearthdata concerning the countries and languages included in the dataset are provided in country_data.csv and language_data.csv to facilitate analysis. 

The data can be explored interactively at: https://f39e09-hannes-essfors.shinyapps.io/wikilingdiv_dashboard/
DOI: 10.5281/zenodo.17598272
title: 3D VNWFET-Based Standard Cell Library

description: The vertical nanowire field effect transistor (VNWFET) is an emerging technology that promises to improve the sustainability of future transistor scaling beyond the limitations of conventional lateral devices. With its 3D gate-all-around (GAA) architecture, such a technology enables designs with improved energy-efficiency as well as reduced footprint and thus interconnect capacitance.  

In this context, we use an accurate executable compact model implemented in Verilog-A with a parameter set fitted to measurements of an experimental fabricated VNWFET device to propose a novel flow toward the generation and verification of standard cell libraries. Our work goes from transistor level design and characterization of basic logic cells up to physical design, verification and parasitic extraction. This then allowed us to generate standard cell libraries based on this VNWFET technology. 

This dataset includes the outcome of this flow presented by the liberty file of standracd cell libraires based on 3D VNWFET devices while including the resitance and capacitance parasitic for the characterization of the cells. The files correspond to two threshold voltages: Regular (RVT) and Low (LVT) and composed of the following cells:



BUFX1_CStatic_JL1

INVX1_CStatic_JL1

NAND2X1_CStatic_JL1

NOR2X1_CStatic_JL1

XOR2X1_CStatic_JL1

Asynch_DFFX1_CStatic_JL1


where the formalism OPnXk_Style_Technology  indicates the Boolean operation OP, the number of inputs n and the drive strength k (k=1 corresponds to 4 NWs per device), as well as the logic design style (i.e., Complementary static logic obtained by using n- and p-type VNWFET) and the technology variant used to implement the cells: Junction-Less Nanowire (JL1). In this data set, we only provide the physical layout (in GDSII format) of the logic cells present in the generated liberty files however, cells with different drive strengths can be provided upon request.
DOI: 10.5281/zenodo.17598273
title: 3D VNWFET-Based Standard Cell Library

description: The vertical nanowire field effect transistor (VNWFET) is an emerging technology that promises to improve the sustainability of future transistor scaling beyond the limitations of conventional lateral devices. With its 3D gate-all-around (GAA) architecture, such a technology enables designs with improved energy-efficiency as well as reduced footprint and thus interconnect capacitance.  

In this context, we use an accurate executable compact model implemented in Verilog-A with a parameter set fitted to measurements of an experimental fabricated VNWFET device to propose a novel flow toward the generation and verification of standard cell libraries. Our work goes from transistor level design and characterization of basic logic cells up to physical design, verification and parasitic extraction. This then allowed us to generate standard cell libraries based on this VNWFET technology. 

This dataset includes the outcome of this flow presented by the liberty file of standracd cell libraires based on 3D VNWFET devices while including the resitance and capacitance parasitic for the characterization of the cells. The files correspond to two threshold voltages: Regular (RVT) and Low (LVT) and composed of the following cells:



BUFX1_CStatic_JL1

INVX1_CStatic_JL1

NAND2X1_CStatic_JL1

NOR2X1_CStatic_JL1

XOR2X1_CStatic_JL1

Asynch_DFFX1_CStatic_JL1


where the formalism OPnXk_Style_Technology  indicates the Boolean operation OP, the number of inputs n and the drive strength k (k=1 corresponds to 4 NWs per device), as well as the logic design style (i.e., Complementary static logic obtained by using n- and p-type VNWFET) and the technology variant used to implement the cells: Junction-Less Nanowire (JL1). In this data set, we only provide the physical layout (in GDSII format) of the logic cells present in the generated liberty files however, cells with different drive strengths can be provided upon request.
DOI: 10.15468/dl.j9u7tk
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chorigyne pendula",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 46 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011185-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18619102
title: Formula 1 Qualifying and Race Results: Hybrid Era (2014–2021)

description: This dataset provides a structured record of qualifying and race classification results from the Formula 1 Hybrid Power Unit Era (2014–2021).

This regulatory period began in 2014 with the introduction of 1.6-litre turbocharged V6 hybrid power units incorporating advanced energy recovery systems (ERS), replacing the previous naturally aspirated V8 engines. The regulations imposed a 15,000 rpm rev limit, fuel-flow restrictions, and hybrid deployment systems including MGU-K and MGU-H components. From 2017 onward, aerodynamic regulation changes significantly increased downforce and overall car performance.

The era was competitively dominated by Mercedes, who won all eight Constructors’ Championships from 2014 to 2021. Lewis Hamilton won the Drivers’ Championship in six of those seasons (2014, 2015, 2017–2020), with Nico Rosberg winning in 2016. The period concluded with Max Verstappen securing the 2021 Drivers’ Championship for Red Bull.

Each entry represents a driver–event classification and includes:



Championship season and round number,

Official Grand Prix name,

Consistent driver identifier and driver name,

Constructor (team) at the event,

Final classified qualifying position,

Official race grid position,

Final classified race finishing position,

Qualifying session times (Q1, Q2, Q3 where applicable), and

Classification status (e.g. Finished, DNF, DSQ).


The dataset has been harmonised to ensure consistent driver identifiers, constructor naming, and session alignment across seasons. Only classification-level information is included; lap-by-lap timing and telemetry data are intentionally excluded.

Typical applications include longitudinal performance analysis, modelling of starting and finishing position relationships, regulatory-era comparisons, and teaching demonstrations in data science, optimisation, and sports analytics.

The dataset and accompanying documentation are provided as CSV and Markdown files.
DOI: 10.5281/zenodo.18619101
title: Formula 1 Qualifying and Race Results: Hybrid Era (2014–2021)

description: This dataset provides a structured record of qualifying and race classification results from the Formula 1 Hybrid Power Unit Era (2014–2021).

This regulatory period began in 2014 with the introduction of 1.6-litre turbocharged V6 hybrid power units incorporating advanced energy recovery systems (ERS), replacing the previous naturally aspirated V8 engines. The regulations imposed a 15,000 rpm rev limit, fuel-flow restrictions, and hybrid deployment systems including MGU-K and MGU-H components. From 2017 onward, aerodynamic regulation changes significantly increased downforce and overall car performance.

The era was competitively dominated by Mercedes, who won all eight Constructors’ Championships from 2014 to 2021. Lewis Hamilton won the Drivers’ Championship in six of those seasons (2014, 2015, 2017–2020), with Nico Rosberg winning in 2016. The period concluded with Max Verstappen securing the 2021 Drivers’ Championship for Red Bull.

Each entry represents a driver–event classification and includes:



Championship season and round number,

Official Grand Prix name,

Consistent driver identifier and driver name,

Constructor (team) at the event,

Final classified qualifying position,

Official race grid position,

Final classified race finishing position,

Qualifying session times (Q1, Q2, Q3 where applicable), and

Classification status (e.g. Finished, DNF, DSQ).


The dataset has been harmonised to ensure consistent driver identifiers, constructor naming, and session alignment across seasons. Only classification-level information is included; lap-by-lap timing and telemetry data are intentionally excluded.

Typical applications include longitudinal performance analysis, modelling of starting and finishing position relationships, regulatory-era comparisons, and teaching demonstrations in data science, optimisation, and sports analytics.

The dataset and accompanying documentation are provided as CSV and Markdown files.
DOI: 10.5281/zenodo.18619105
title: Formula 1 Qualifying and Race Results: Ground Effect Era (2022–2025)

description: This dataset provides a structured record of qualifying and race classification results from the Formula 1 Ground Effect Era (2022–2025).

Beginning in 2022, Formula 1 introduced a new aerodynamic framework centred on ground-effect floor designs aimed at reducing wake turbulence and enabling closer on-track racing. The regulations simplified upper-body aerodynamic surfaces, eliminated bargeboards, redesigned front and rear wings, and placed greater emphasis on underfloor venturi tunnels for downforce generation, while maintaining the 1.6-litre turbo-hybrid power unit architecture used in the preceding era.

During this period, sprint weekend formats were introduced and subsequently revised, altering the structure of selected race weekends and the determination of starting grids.

The competitive landscape during this era was more distributed than in the preceding period. Red Bull Racing won the Constructors’ Championships in 2022 and 2023, while McLaren secured the Constructors’ titles in 2024 and 2025. Max Verstappen won the Drivers’ Championship in 2022, 2023, and 2024, with Lando Norris winning the 2025 Drivers’ Championship.

Each entry represents a driver–event classification and includes:



Championship season and round number,

Official Grand Prix name,

Consistent driver identifier and driver name,

Constructor (team) at the event,

Final classified qualifying position,

Official race grid position,

Final classified race finishing position,

Net position change during the race (finish position minus grid position),

Qualifying session times (Q1, Q2, Q3 where applicable), and

Classification status (e.g. Finished, DNF, DSQ).


The dataset has been harmonised to ensure consistent driver identifiers, constructor naming, and alignment across varying weekend formats, including sprint-affected race grids.

Typical applications include comparative analysis across regulatory eras, modelling of positional movement and race outcomes, and use in teaching contexts involving data science and sports analytics.

The datasets and accompanying documentation are provided as CSV and Markdown files.