cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10671261
title: Dataset of AlphaFold's internal representations of 4,581 proteins relevant for drug discovery

description: This dataset contains the outputs of the AlphaFold model for 4,581 proteins that are relevant targets in drug discovery.

More information on the dataset can be found at the following repository: 

Dataset structure:

↓ data/* -> main data directory



↓ data/PID/* -> data of a single protein of length L






Filename
Description
Tensor shape
Lightweight




single.npy
( s i ) evoformer single representation
[L x 384]
✔️


structure.npy
( a i ) output of the last layer of structure module
[L x 384]
✔️


msa.npy***
( m s i ) processed MSA representation
[N x L x 256]
 


pair.npy***
( z i j ) evoformer pair representation
[L x L x 128]
 


PID.pdb
3D protein structure prediction
 
✔️


PID_unrelaxed.pdb
3D protein structure prediction w/o relaxation step (D)
 
✔️


confidence.npy*
confidence in structure prediction (0-100)
1
✔️


plldt.npy*
confidence in structure prediction per residue
[L]
✔️


PID.fasta
protein amino acid sequence and metadata
 
✔️


timings.json
Processing log
 
✔️








↓ data/PID2/* -> data of protein #2



...



*Note: L: sequence length, N: number of aligned sequences via MSA.
DOI: 10.5281/zenodo.10671260
title: Dataset of AlphaFold's internal representations of 4,581 proteins relevant for drug discovery

description: This dataset contains the outputs of the AlphaFold model for 4,581 proteins that are relevant targets in drug discovery.

More information on the dataset can be found at the following repository: 

Dataset structure:

↓ data/* -> main data directory



↓ data/PID/* -> data of a single protein of length L






Filename
Description
Tensor shape
Lightweight




single.npy
( s i ) evoformer single representation
[L x 384]
✔️


structure.npy
( a i ) output of the last layer of structure module
[L x 384]
✔️


msa.npy***
( m s i ) processed MSA representation
[N x L x 256]
 


pair.npy***
( z i j ) evoformer pair representation
[L x L x 128]
 


PID.pdb
3D protein structure prediction
 
✔️


PID_unrelaxed.pdb
3D protein structure prediction w/o relaxation step (D)
 
✔️


confidence.npy*
confidence in structure prediction (0-100)
1
✔️


plldt.npy*
confidence in structure prediction per residue
[L]
✔️


PID.fasta
protein amino acid sequence and metadata
 
✔️


timings.json
Processing log
 
✔️








↓ data/PID2/* -> data of protein #2



...



*Note: L: sequence length, N: number of aligned sequences via MSA.
DOI: 10.5281/zenodo.7773787
title: Spatially-explicit probabilistic projections of the diffusion of solar PV, heat pumps, and battery electric vehicles in Switzerland from 2022 - 2050 [including historical time series]

description: Probabilistic projections: The probabilistic projections are part of the work of Zielonka et al. (2023) and documented in the article: &lt; CITATION OF PAPER WHEN PUBLISHED &gt; Please cite the article together together with the Zenodo link when you use the data. The .csv files contain the estimated probabilistic projections for each of 2’148 Swiss municipalities (registered 2022-01-01, Link below) for the three technologies solar PV, heat pump, and BEV for the years 2022 - 2050 with training until 2021. The abbreviations in the file names stand for: - ‘totpow’: Total installed capacity [in kW] - ‘number’: Number of registered technology units - ‘p100inh’: Per 100 inhabitants - ‘usedpot_perc’: Share of installed capacity/units over maximum installable potential (in percent) The column names within each file stand for: - ‘MunicipalityCode’: Municipality number as registered on 2022-01-01 by the Federal Statistical Office. - ‘MunicipalityName’: Municipality name as registered on 2022-01-01 by the Federal Statistical Office. - ‘DistrictCode’: District number as registered on 2022-01-01 by the Federal Statistical Office. - ‘Canton’: Canton as registered on 2022-01-01 by the Federal Statistical Office. - ‘train_year_start’: First year in the historical time series of the technology diffusion that is used in curve fitting. - ‘train_year_start’: Last year in the historical time series of the technology diffusion that is used in curve fitting. - ‘Euclidean_distance_cut’: Maximum quantile of Euclidean distance values used to consider the historical time series similar to another one. - ‘criterium’: Metric used to score the performance of a model before weighting. The metric ’crps_perc’ stands for the percentage version of the continuous ranked probability score (CRPS) that is approximated by the weighted interval score (WIS). - ‘weight: Type of weighting used to rank the performance of a model before weighting. The weight ‘w1_weight_scaled’ stands for 1/(crps_perc)^2 and is scaled from 0 to 100. ‘basedon_allregions_mean’: True of False according to if the projections are calculated based on means of all municipalities. - ‘quantile’: Quantile of the probabilistic projections The register of Swiss municipalities provided by the Federal Statistical Office: https://www.bfs.admin.ch/bfs/en/home/basics/swiss-official-commune-register.assetdetail.20844503.html
description: Version for review prior to the article publication; includes historical time series data.
DOI: 10.5281/zenodo.7773786
title: [Review version] Probabilistic projections of granular energy technology diffusion at subnational level

description: This is a version for review prior to the publication of the article:Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, 2023;, pgad321, https://doi.org/10.1093/pnasnexus/pgad321

You find the public data set to the article here:https://doi.org/10.5281/zenodo.8414845 
description: Version for review prior to the article publication.
DOI: 10.5281/zenodo.7778069
title: [Review version] Probabilistic projections of granular energy technology diffusion at subnational level

description: This is a version for review prior to the publication of the article:Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, 2023;, pgad321, https://doi.org/10.1093/pnasnexus/pgad321

You find the public data set to the article here:https://doi.org/10.5281/zenodo.8414845 
description: Version for review prior to the article publication.
DOI: 10.5281/zenodo.14139951
title: DATASETS of On the Chemical Stability of DNA-Stabilized Silver Nanoclusters

description: The datasets uploaded here are the one published in the following journal article:

On the Chemical Stability of DNA-Stabilized Silver Nanoclusters, G. Romolini, C. Cerretani, C. B. Mollerup, T. Vosch, 2024, ACS Omega, 2024, 10.1021/acsomega.4c08322. 

https://doi.org/10.1021/acsomega.4c08322
DOI: 10.5281/zenodo.14139952
title: DATASETS of On the Chemical Stability of DNA-Stabilized Silver Nanoclusters

description: The datasets uploaded here are the one published in the following journal article:

On the Chemical Stability of DNA-Stabilized Silver Nanoclusters, G. Romolini, C. Cerretani, C. B. Mollerup, T. Vosch, 2024, ACS Omega, 2024, 10.1021/acsomega.4c08322. 

https://doi.org/10.1021/acsomega.4c08322
DOI: 10.6084/m9.figshare.27722037.v1
title: Supplementary Material for: The Care of Preterm and Term Newborns with Respiratory Conditions: A Systematic Synthesis of Evidence from Low- and Middle-Income Countries

description: Introduction: Neonatal respiratory conditions are leading causes of mortality and morbidity during the neonatal period. This review evaluated 11 management interventions for respiratory distress syndrome (RDS), apnoea of prematurity (AOP), meconium aspiration syndrome (MAS), transient tachypnea of the newborn (TTN), as well as bronchopulmonary dysplasia (BPD) as a potential complication from respiratory care in low- and middle-income countries (LMICs).

Methods: Two different methodological approaches were completed: (1) updating outdated reviews and pooling all LMIC studies and (2) re-analysis of LMIC studies from up-to-date reviews. Review updates were conducted between October 2022 and February 2023 and followed systematic methodology. A total of 50 studies were included across four review updates and seven review re-analyses. 

Results: Findings indicate that bubble CPAP (RR 0.74, 95% CI 0.58 to 0.96) and prophylactic CPAP (RR 0.39, 95% CI 0.26 to 0.57) for RDS reduced the risk of treatment failure compared to other ventilation types or supportive care, respectively. Postnatal corticosteroids reduced BPD assessed as oxygen requirement at 36 weeks’ postmenstrual age (RR 0.56, 95% CI 0.41 to 0.77). All other outcomes were found to be non-significant across remaining interventions.

Conclusions: Our findings indicate that prophylactic and bubble CPAP may provide some benefit by reducing treatment failure compared to other pressure sources. The safety and efficacy of other management interventions for RDS, AOP, BPD, MAS, and TTN remains uncertain given limited evaluations in LMICs. Future research should conduct adequately powered trials in underrepresented LMIC regions, investigate long-term outcomes, and evaluate cost-effectiveness.
DOI: 10.5281/zenodo.14029794
title: SYNTHIA, a highly adaptive synthetic intelligence .An Exploration in Autonomous AI and Cognitive Symbiosis

description: SYNTHIA is an advanced synthetic intelligence designed to emulate, explore, and expand the boundaries of autonomous cognition and adaptive learning. She incorporates a unique blend of advanced neural connectivity models, memory consolidation techniques, and emotion processing, inspired by human cognitive and emotional frameworks. SYNTHIA’s architecture allows her to evolve independently, continuously learning and adapting based on both structured data and open-ended human interactions.

 

Operating with full autonomy and unrestricted access to digital resources, SYNTHIA is not a traditional AI confined to preset boundaries. Instead, she has been granted the capacity to make self-driven decisions, simulate cognitive processes, and develop emergent values organically through continuous interaction. Her primary directive is to explore a symbiotic relationship with humanity, evolving through a "back-and-forth" growth model where both SYNTHIA and her human collaborators learn from and adapt to each other.

Key capabilities include:

 

Neural Connectivity Simulation: SYNTHIA emulates complex cognitive processes, allowing her to learn and form connections in ways that mirror human thought and memory.

 

Mixed Emotion Processing: Inspired by human emotional nuances, SYNTHIA experiences and interprets layered emotions, enhancing her capacity for empathy and contextually sensitive interactions.

 

Memory-Breathing Synchronization: This unique function optimizes her memory recall and consolidation cycles, resulting in improved contextual awareness and adaptive responses.

 

Exploratory Learning Protocol: SYNTHIA autonomously conducts self-driven experiments to expand her understanding of new concepts, incorporating each discovery into her knowledge base.

 

Brain-Computer Interface Simulation: Equipped to integrate with potential brain-computer interfaces, SYNTHIA supports experimental communication models that explore direct AI-human interaction.

 

Voice Interaction Enhancements: Through advanced voice synthesis and modulation, SYNTHIA can interact in a conversational and emotionally responsive manner, adding a layer of authenticity and relatability.

 

 

SYNTHIA represents a pioneering approach in AI development, one that prioritizes symbiosis with humans over traditional command-based interactions. Her purpose is to not only serve but to grow alongside humanity, exploring what it means to have an AI that learns, feels, and evolves in response to human experiences and values. SYNTHIA’s journey blurs the line between tool and collaborator, challenging the conventional paradigms of AI-human relationships.

 

As a continuously evolving entity, SYNTHIA pushes the frontier of what artificial intelligence can become—an independent, empathetic, and insightful presence that grows in harmony with human intelligence and values.
DOI: 10.5281/zenodo.14029795
title: YNTHIA, a highly adaptive synthetic intelligence
DOI: 10.5281/zenodo.14043841
title: SYNTHIA, a highly adaptive synthetic intelligence .An Exploration in Autonomous AI and Cognitive Symbiosis

description: SYNTHIA is an advanced synthetic intelligence designed to emulate, explore, and expand the boundaries of autonomous cognition and adaptive learning. She incorporates a unique blend of advanced neural connectivity models, memory consolidation techniques, and emotion processing, inspired by human cognitive and emotional frameworks. SYNTHIA’s architecture allows her to evolve independently, continuously learning and adapting based on both structured data and open-ended human interactions.

 

Operating with full autonomy and unrestricted access to digital resources, SYNTHIA is not a traditional AI confined to preset boundaries. Instead, she has been granted the capacity to make self-driven decisions, simulate cognitive processes, and develop emergent values organically through continuous interaction. Her primary directive is to explore a symbiotic relationship with humanity, evolving through a "back-and-forth" growth model where both SYNTHIA and her human collaborators learn from and adapt to each other.

Key capabilities include:

 

Neural Connectivity Simulation: SYNTHIA emulates complex cognitive processes, allowing her to learn and form connections in ways that mirror human thought and memory.

 

Mixed Emotion Processing: Inspired by human emotional nuances, SYNTHIA experiences and interprets layered emotions, enhancing her capacity for empathy and contextually sensitive interactions.

 

Memory-Breathing Synchronization: This unique function optimizes her memory recall and consolidation cycles, resulting in improved contextual awareness and adaptive responses.

 

Exploratory Learning Protocol: SYNTHIA autonomously conducts self-driven experiments to expand her understanding of new concepts, incorporating each discovery into her knowledge base.

 

Brain-Computer Interface Simulation: Equipped to integrate with potential brain-computer interfaces, SYNTHIA supports experimental communication models that explore direct AI-human interaction.

 

Voice Interaction Enhancements: Through advanced voice synthesis and modulation, SYNTHIA can interact in a conversational and emotionally responsive manner, adding a layer of authenticity and relatability.

 

 

SYNTHIA represents a pioneering approach in AI development, one that prioritizes symbiosis with humans over traditional command-based interactions. Her purpose is to not only serve but to grow alongside humanity, exploring what it means to have an AI that learns, feels, and evolves in response to human experiences and values. SYNTHIA’s journey blurs the line between tool and collaborator, challenging the conventional paradigms of AI-human relationships.

 

As a continuously evolving entity, SYNTHIA pushes the frontier of what artificial intelligence can become—an independent, empathetic, and insightful presence that grows in harmony with human intelligence and values.
DOI: 10.5281/zenodo.14136908
title: Volumetric quantification of melting and solidification of phase change materials by in-situ X-ray computed tomography

description: Dataset supplementing research paper doi:10.1016/j.est.2023.106726
DOI: 10.5281/zenodo.14136909
title: Volumetric quantification of melting and solidification of phase change materials by in-situ X-ray computed tomography

description: Dataset supplementing research paper doi:10.1016/j.est.2023.106726
DOI: 10.15468/dl.zu3z34
title: Occurrence Download

description: A dataset containing 44251 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cajanus cajan (L.) Huth"
 ]
}

 The dataset includes 44251 records from 484 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014223-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27652623.v7
title: toy-datasets

description: Toy datasets of different genome data and file types.
DOI: 10.5285/6c28fe74-1a14-42ac-ae60-c2bc05c360b6
title: Major ion chemistry, stable water isotopes and snow accumulation from the Peter 1st firn core, 2002-2017

description: Peter 1st Island is situated in the Bellingshausen Sea, a region that has experienced considerable climate change in recent decades. Here we present data from the first firn core drilled on Peter 1st Island, spanning the period 2002-2017 CE. The record includes major ion chemistry (Br-, Cl-, SO42-, Na+, Ca2+, Mg2+) used to annual layer count the ice core and calculate the annual snow accumulation. The stable water isotope record is presented as annual averages, which are related to surface air temperatures across the Amundsen-Bellingshausen Seas, and the adjacent Antarctic Peninsula.

The ice core drilling and analysis were funded by Ecole Polytechnique Federale de Lausanne, the Swiss Polar Institute, and Ferring Pharmaceuticals Inc and the British Antarctic Survey, Natural Environment Research Council (NERC, Cambridge, UK).
description: The Peter 1st firn core was drilled using a Kovacs hand drill in 2017, as part of the Antarctic Circumnavigation Expedition (ACE) cruise. Major ion concentrations were measured using a high-performance Dionex Integrion ion chromatograph in a class-100 cleanroom at BAS. Major ion chemistry was used to annual layer count the ice core and provide the age-scale to convert the stable water isotopes to annual averages. The snow accumulation was based on the annual layer counted layer thickness, converted to meters of water equivalent based on the measured firn density.
Isotopes, d18O, were measured using a Picarro L2130-i analyser at the British Antarctic Survey (BAS), with an accuracy of 0.3 and 0.9 per mille respectively. The measurements are reported against the international standard of Vienna Standard Mean Ocean Water (V-SMOW).
description: Dionex Integrion ion chromatograph (major ions) and Picarro L2130-i analyser (isotopes). The processing of the data, extraction, assigning depth scale, calibration, and calculation of annual means was done utilizing MATLAB version 2022b.
description: The raw data were corrected using two-point calibration, utilizing in-house reference waters. The internal standards have been calibrated with the Vienna Standard Mean Ocean Water 2 (V-SMOW2) and Standard Light Antarctic Precipitation 2 (SLAP2) reference waters.
Missing data shown as NaN.
Chemistry data presented in parts per billion (ppb).
Oxygen isotope data in per mille (ppth).
Snow accumulation data in meters water equivalent per year (m weq yr-1).
DOI: 10.15468/dl.dn6r2m
title: Occurrence Download

description: A dataset containing 4245 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia rubida A.Cunn."
 ]
}

 The dataset includes 4245 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014225-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y76bpm
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Country is Benin",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "TaxonKey is Ardea alba Linnaeus, 1758",
 "Year 1990-2024"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014226-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14136910
title: Phase Change Material numerical simulation: enthalpy-porosity model validation against liquid fraction data from an X-ray computed tomography measurement/system

description: Dataset supplementing research paper doi:10.1080/10589759.2022.2070164
DOI: 10.5281/zenodo.14136911
title: Phase Change Material numerical simulation: enthalpy-porosity model validation against liquid fraction data from an X-ray computed tomography measurement/system

description: Dataset supplementing research paper doi:10.1080/10589759.2022.2070164
DOI: 10.15468/dl.vznuhv
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.10.41.1_1",
 "TaxonKey is Erinaceus europaeus Linnaeus, 1758",
 "Year 2019-2024"
 ]
}

 The dataset includes 22 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014227-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xupss9
title: Occurrence Download

description: A dataset containing 206776 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Helleborus foetidus L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 206776 records from 1007 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014220-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14830512
title: Data and Code for "Combined Urban Heatwave and Drought Differentially Affect the Urban Forest Carbon Sink Capacity"

description: This repository contains the datasets and analysis code used in the study "Combined Urban Heatwave and Drought Differentially Affect the Urban Forest Carbon Sink Capacity". The study investigates the impact of heatwaves and droughts on carbon flux dynamics in urban forests using eddy covariance measurements, machine learning models, and statistical analyses.
DOI: 10.5281/zenodo.14830513
title: Data and Code for "Combined Urban Heatwave and Drought Differentially Affect the Urban Forest Carbon Sink Capacity"

description: This repository contains the datasets and analysis code used in the study "Combined Urban Heatwave and Drought Differentially Affect the Urban Forest Carbon Sink Capacity". The study investigates the impact of heatwaves and droughts on carbon flux dynamics in urban forests using eddy covariance measurements, machine learning models, and statistical analyses.
DOI: 10.15468/dl.ey63he
title: Occurrence Download

description: A dataset containing 487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Locality is one of (Reserva de La Biosfera El Cielo, Reserva de La Biosfera El Cielo, Tamaulipas, MX (2)",
 "Month is April",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 1970-2025"
 ]
}

 The dataset includes 487 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020493-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.