cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10846253
title: MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

description: MetaFlux is a global, long-term carbon flux dataset of gross primary production and ecosystem respiration that is generated using meta-learning. The principle of meta-learning stems from the need to solve the problem of learning in the face of sparse data availability. Data sparsity is a prevalent challenge in climate and ecology science. For instance, in-situ observations tend to be spatially and temporally sparse. This issue can arise from sensor malfunctions, limited sensor locations, or non-ideal climate conditions such as persistent cloud cover. The lack of high-quality continuous data can make it difficult to understand many climate processes that are otherwise critical. The machine-learning community has attempted to tackle this problem by developing several learning approaches, including meta-learning that learns how to learn broad features across tasks to better infer other poorly sampled ones. In this work, we applied meta-learning to solve the problem of upscaling continuous carbon fluxes from sparse observations. Data scarcity in carbon flux applications is particularly problematic in the tropics and semi-arid regions, where only around 8–11% of long-term eddy covariance stations are currently operational. Unfortunately, these regions are important in modulating the global carbon cycle and its interannual variability. In general, we find that meta-trained machine models, including multi-layer perceptrons (MLP), long-short-term memory (LSTM), and bi-directional LSTM (BiLSTM), have lower validation errors on flux estimates by 9–16% when compared to their non-meta-trained counterparts. In addition, meta-trained models are more robust to extreme conditions, with 4–24% lower overall errors. Finally, we use an ensemble of meta-trained deep networks to generate a global product of ecosystem-scale photosynthesis and respiration fluxes from in-situ observations to daily and monthly global products at a 0.25-degree spatial resolution from 2001 to 2023, called "MetaFlux". We also checked for the seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed state-of-the-art machine learning upscaling models, especially in critical semi-arid and tropical regions.
DOI: 10.5281/zenodo.7761881
title: MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

description: MetaFlux is a global, long-term carbon flux dataset of gross primary production and ecosystem respiration that is generated using meta-learning. The principle of meta-learning stems from the need to solve the problem of learning in the face of sparse data availability. Data sparsity is a prevalent challenge in climate and ecology science. For instance, in-situ observations tend to be spatially and temporally sparse. This issue can arise from sensor malfunctions, limited sensor locations, or non-ideal climate conditions such as persistent cloud cover. The lack of high-quality continuous data can make it difficult to understand many climate processes that are otherwise critical. The machine-learning community has attempted to tackle this problem by developing several learning approaches, including meta-learning that learns how to learn broad features across tasks to better infer other poorly sampled ones. In this work, we applied meta-learning to solve the problem of upscaling continuous carbon fluxes from sparse observations. Data scarcity in carbon flux applications is particularly problematic in the tropics and semi-arid regions, where only around 8–11% of long-term eddy covariance stations are currently operational. Unfortunately, these regions are important in modulating the global carbon cycle and its interannual variability. In general, we find that meta-trained machine models, including multi-layer perceptrons (MLP), long-short-term memory (LSTM), and bi-directional LSTM (BiLSTM), have lower validation errors on flux estimates by 9–16% when compared to their non-meta-trained counterparts. In addition, meta-trained models are more robust to extreme conditions, with 4–24% lower overall errors. Finally, we use an ensemble of meta-trained deep networks to generate a global product of ecosystem-scale photosynthesis and respiration fluxes from in-situ observations to daily and monthly global products at a 0.25-degree spatial resolution from 2001 to 2021, called "MetaFlux". We also checked for the seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed state-of-the-art machine learning upscaling models, especially in critical semi-arid and tropical regions.
DOI: 10.6084/m9.figshare.25448104
title: <b>表 1.</b> 每个 XRCC 家族成员的多变量 Cox 回归分析以及 OS 的常见临床特征可识别不同的癌症类型。

description: <b>表 1.</b> 每个 XRCC 家族成员的多变量 Cox 回归分析以及 OS 的常见临床特征可识别不同的癌症类型。
DOI: 10.6084/m9.figshare.25448104.v2
title: <b>表 1.</b> 每个 XRCC 家族成员的多变量 Cox 回归分析以及 OS 的常见临床特征可识别不同的癌症类型。

description: <b>表 1.</b> 每个 XRCC 家族成员的多变量 Cox 回归分析以及 OS 的常见临床特征可识别不同的癌症类型。
DOI: 10.6084/m9.figshare.25448104.v1
title: <b>Table 1.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for OS acorss different cancer types.

description: <b>Table 1.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for OS acorss different cancer types.
DOI: 10.6084/m9.figshare.25448122
title: Artifacts for "AssetHarvester: It's not just about the Secrets in Software Artifacts"

description: The artifact contains a sample dataset of AssetBench and the implementation details of AssetHarvester.
DOI: 10.6084/m9.figshare.25448122.v1
title: Artifacts for "AssetHarvester: It's not just about the Secrets in Software Artifacts"

description: The artifact contains a sample dataset of AssetBench and the implementation details of AssetHarvester.
DOI: 10.6084/m9.figshare.25447960
title: EFFECTS OF BLENDED NPS FERTILIZER RATES ON YIELD COMPONENTS AND YIELD OF COMMON BEAN (Phaseolus vulgaris L.) VARIETIES IN BEDESA FIELD GENE BANK, BEDESA DISTRICT, ETHIOPIA

description: This study looked at how different amounts of Blended NPS fertilizer affect the growth and yield of different types of common beans in Bedesa, Ethiopia. They tested three bean varieties with five fertilizer rates and found that variety SER-119 and 150 kg of fertilizer per hectare gave the best results in terms of yield and profit. This suggests that using SER-119 beans with 150 kg of NPS fertilizer per hectare is the most effective way to grow beans in that area.
DOI: 10.6084/m9.figshare.25448149
title: <b>Table 2.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DFS acorss different cancer types.

description: <b>Table 2.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DFS acorss different cancer types.
DOI: 10.6084/m9.figshare.25448149.v1
title: <b>Table 2.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DFS acorss different cancer types.

description: <b>Table 2.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DFS acorss different cancer types.
DOI: 10.6084/m9.figshare.25448146
title: Syntenic DEGs and proximal genetic elements

description: The detailed list of syntenic DEGs and proximal genetic elements
DOI: 10.6084/m9.figshare.25448146.v1
title: Syntenic DEGs and proximal genetic elements

description: The detailed list of syntenic DEGs and proximal genetic elements
DOI: 10.6084/m9.figshare.25448158
title: <b>Table 3.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DSS acorss different cancer types.

description: <b>Table 3.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DSS acorss different cancer types.
DOI: 10.6084/m9.figshare.25448158.v1
title: <b>Table 3.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DSS acorss different cancer types.

description: <b>Table 3.</b> Multivariate Cox-regression analysis of each XRCC family member and common clinical features for DSS acorss different cancer types.
DOI: 10.15468/dl.8bda3f
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Echinosepala aspasicensis (Rchb.f.) Pridgeon & M.W.Chase, Madisonia articulata (Lindl.) Toscano & E.C.Smidt, Madisonia bradei (Schltr.) Toscano & E.C.Smidt, Madisonia brasilica (Luer & Toscano) Toscano & E.C.Smidt, Madisonia carrisii (Brade) Toscano & E.C.Smidt, Madisonia gomesii-ferreirae (Pabst) Toscano & E.C.Smidt, Madisonia ianthina (E.M.Pessoa & F.Barros) Toscano & E.C.Smidt, Madisonia kerrii (Braga) Luer, Madisonia spiculifera (Lindl.) Toscano & E.C.Smidt, Madisonia vandenbergii (Chiron) Toscano & E.C.Smidt)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 113 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021199-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yty3tw
title: Occurrence Download

description: A dataset containing 46489 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation)",
 "Country is Colombia",
 "Geometry MULTIPOLYGON(((-73.8508 8.22823,-74.7925 5.8077,-74.27497 5.38751,-74.11771 5.49459,-73.31507 4.6313,-73.05331 4.56361,-72.6447 4.18011,-71.44326 4.52911,-70.52064 5.22107,-69.29049 6.09756,-70.05934 7.05093,-70.7766 7.14825,-71.04204 7.05407,-71.76985 7.13113,-72.34353 7.17394,-73.8508 8.22823)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Melastomataceae, Rubiaceae, Araliaceae, Euphorbiaceae, Lauraceae, Moraceae, Urticaceae, Solanaceae, Cunoniaceae, Winteraceae, Arecaceae, Clusiaceae, Bignoniaceae, Myrtaceae, Primulaceae, Ericaceae, Fagaceae, Juglandaceae, Meliaceae, Anacardiaceae, Annonaceae, Piperaceae, Fabaceae, Rosaceae)"
 ]
}

 The dataset includes 46489 records from 140 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021198-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10104800
title: Figure data for: Free-electron interaction with nonlinear optical states in microresonators

description: This dataset contains the figure data and code for the paper "Free-electron interaction with nonlinear optical states in microresonators".
DOI: 10.5281/zenodo.10104801
title: Figure data for: Free-electron interaction with nonlinear optical states in microresonators

description: This dataset contains the figure data and code for the paper "Free-electron interaction with nonlinear optical states in microresonators".
DOI: 10.5281/zenodo.10432745
title: e-DIPLOMA - Dataset: The values of using disruptive technologies

description: This is the dataset "The values of using disruption technologies" of the e-DIPLOMA project and includes the following documents:



Values.xlsx

Scenarios.ppt

ReadmeFile.rtf


The data were collected in the values-workshop. They describe how the practice based example learning scenarios with disruptive technologies are perceived regarding values. The data were collected in workshops held in several European countries. The participants could read about the learning design scenarios with disruptive technologies and then discuss the values they perceived regarding these learning situations.In the workshops the instrument with 45 values names and descriptions was provided to elicit values. The values could be associated with four different learning scenarios with disruptive technologies. The group interview was held at the workshop at 2,5 h, the groups jointly discussed the values. The participants of the group workshop were heterogeneous: students, educators, technical support personnel at institutes, educational technology developers. There were 4 groups with 4-5 persons in each workshop. The values were collected using the anonymous online survey approach. The workshops were conducted in the national languages and data were translated into English.
DOI: 10.5281/zenodo.10432746
title: e-DIPLOMA - Dataset: The values of using disruptive technologies

description: This is the dataset "The values of using disruption technologies" of the e-DIPLOMA project and includes the following documents:



Values.xlsx

Scenarios.ppt

ReadmeFile.rtf


The data were collected in the values-workshop. They describe how the practice based example learning scenarios with disruptive technologies are perceived regarding values. The data were collected in workshops held in several European countries. The participants could read about the learning design scenarios with disruptive technologies and then discuss the values they perceived regarding these learning situations.In the workshops the instrument with 45 values names and descriptions was provided to elicit values. The values could be associated with four different learning scenarios with disruptive technologies. The group interview was held at the workshop at 2,5 h, the groups jointly discussed the values. The participants of the group workshop were heterogeneous: students, educators, technical support personnel at institutes, educational technology developers. There were 4 groups with 4-5 persons in each workshop. The values were collected using the anonymous online survey approach. The workshops were conducted in the national languages and data were translated into English.
DOI: 10.5281/zenodo.10432815
title: e-DIPLOMA - Dataset: European remote e-learning ecosystem survey data

description: This is the dataset "European remote e-learning ecosystem survey data" of the e-DIPLOMA project.

In 2022 the evaluation of the European tertiary training ecosystem capacity for using disruptive technologies in practice based e-learning was explored. It was done in the eDiploma project WP2. The research problem was: What are the main gaps in tertiary education in the institutional capacity to perform practice based e-learning with disruptive technologies? Three survey instruments were developed for three target groups in institutions: technology specialists, educators and students. The survey was composed of four blocks of capacity elements: 





infrastructural capacities, 




normative and regulatory capacities (institutional level), 




teaching cultures (community level), 




competences, attitudes and values (personal level). 



The data were collected with the anonymous web based survey approach in countries: Spain, Estonia, Hungary, Bulgaria, Italy, Cyprus. 

In each HEI or VET institution the respondents were:





Technical and didactical support staff: educational technologist, IT or technical support specialists, lecturers responsible for technology training, Digital policy administrative specialist




Lecturers or researchers who have experiences with some forms of group-learning or practice based learning




Students from the institution who have experiences with some forms of group-learning or practice based learning / to be spread among each institution, so that different areas students respond, these should not be one group from one class only)



The answers were collected totally from the following number of the technology specialists-experts (N=96), the educators (N=351), and the students (N=516). The generalizability of the data is limited due to the sampling structure: it was not attempted to reach regional coverage because countries in our sample differ greatly in size. In Estonia responses were collected from 9 institutions (3 vocational schools and 6 HEIs). In Bulgaria responses were from 3 institutions (all HEIs). In Cyprus responses were from 3 institutions (all HEIs). In Hungary responses were from 6 institutions (1 vocational school and 5 HEIs). In Spain responses were from 116 institutions (28 high schools, 41 vocational schools, 47 HEIs). In Italy responses were from 9 institutions (4 HEIs and 5 social enterprises).
DOI: 10.5281/zenodo.10432816
title: e-DIPLOMA - Dataset: European remote e-learning ecosystem survey data

description: This is the dataset "European remote e-learning ecosystem survey data" of the e-DIPLOMA project.

In 2022 the evaluation of the European tertiary training ecosystem capacity for using disruptive technologies in practice based e-learning was explored. It was done in the eDiploma project WP2. The research problem was: What are the main gaps in tertiary education in the institutional capacity to perform practice based e-learning with disruptive technologies? Three survey instruments were developed for three target groups in institutions: technology specialists, educators and students. The survey was composed of four blocks of capacity elements: 





infrastructural capacities, 




normative and regulatory capacities (institutional level), 




teaching cultures (community level), 




competences, attitudes and values (personal level). 



The data were collected with the anonymous web based survey approach in countries: Spain, Estonia, Hungary, Bulgaria, Italy, Cyprus. 

In each HEI or VET institution the respondents were:





Technical and didactical support staff: educational technologist, IT or technical support specialists, lecturers responsible for technology training, Digital policy administrative specialist




Lecturers or researchers who have experiences with some forms of group-learning or practice based learning




Students from the institution who have experiences with some forms of group-learning or practice based learning / to be spread among each institution, so that different areas students respond, these should not be one group from one class only)



The answers were collected totally from the following number of the technology specialists-experts (N=96), the educators (N=351), and the students (N=516). The generalizability of the data is limited due to the sampling structure: it was not attempted to reach regional coverage because countries in our sample differ greatly in size. In Estonia responses were collected from 9 institutions (3 vocational schools and 6 HEIs). In Bulgaria responses were from 3 institutions (all HEIs). In Cyprus responses were from 3 institutions (all HEIs). In Hungary responses were from 6 institutions (1 vocational school and 5 HEIs). In Spain responses were from 116 institutions (28 high schools, 41 vocational schools, 47 HEIs). In Italy responses were from 9 institutions (4 HEIs and 5 social enterprises).
DOI: 10.6084/m9.figshare.24912195.v1
title: Supplementary Material for: White blood cell counts and future relapse in ulcerative colitis under low-dose thiopurine treatment in real-world practice: a three year Japanese multi-center retrospective cohort study.

description: Introduction
Whether white blood cell (WBC) counts are predictors for the effectiveness of thiopurine treatment in ulcerative colitis (UC) has been inconclusive in previous studies with small sample sizes. We investigated the association between WBC counts and future relapses in UC patients in a large-scale multi-center study.
Methods
This retrospective cohort study enrolled a total of 723 UC patients in remission from 33 hospitals and followed up for three years. Relapse was defined as a need for treatment intensification. The risk of relapse was compared among patients with the baseline WBC counts&lt;3000/µL (N=31), 3000–4000/µL (N=167), 4000–5000/µL (N=241), and ≥5000/µL (N=284) using a Cox regression model analysis. Moreover, exploratory analyses were conducted to identify other factors predicting relapse.
Results
During a median follow-up period of 1095 (interquartile range, 1032–1119) days, relapse occurred in 17.2% (125/723). In a crude analysis, WBC counts were not associated with relapse; hazard ratios (HRs) [95% confidence interval (CI)] were 1.50 [0.74–3.06], 1.02 [0.66–1.59] and 0.67 [0.43–1.05] in WBC&lt;3000/µL, 3000–4000/µL, and 4000–5000/µL groups, respectively (WBC≥5000/µL group, as reference). Multivariable-adjusted analyses showed similar results; HRs [95% CI] were 1.21 [0.59–2.49], 1.08 [0.69–1.69], and 0.69 [0.44–1.07], in &lt;3000/µL, 3000–4000/µL, and 4000–5000/µL group, respectively. In the exploratory analyses, thiopurine use &lt;1 year and a mean corpuscular volume &lt;90 fL were predictors for relapse.
Discussion/Conclusion
WBC counts were not predictors for future relapses in patients with UC treated with thiopurine as a maintenance therapy.
DOI: 10.6084/m9.figshare.24104067
title: <b>The strengthened impact of water availability at interannual and decadal timescales on vegetation GPP</b>

description: This data used to analysis the impact of muti-timescales of scPDSI on vegetation GPP
DOI: 10.6084/m9.figshare.24104067.v4
title: <b>The strengthened impact of water availability at interannual and decadal timescales on vegetation GPP</b>

description: This data used to analysis the impact of muti-timescales of scPDSI on vegetation GPP
DOI: 10.6084/m9.figshare.24104067.v3
title: <b>The strengthened impact of water availability at interannual and decadal timescales on vegetation GPP</b>

description: This data used to analysis the impact of muti-timescales of scPDSI on vegetation GPP
DOI: 10.5517/ccdc.csd.cc27k1wr
title: CCDC 2071974: Experimental Crystal Structure Determination

description: Related Article: Jeong Heon Lee, Kyeong-Im Hong, Woo Hyeok Choi, Younghun Kim, Woo-Dong Jang|2023|CSD Communication|||
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.24912321
title: Ophiura_sarsiiLutken.TRF.gff

description: Repeat sequence annotation of Ophiura sarsii with TRF
DOI: 10.6084/m9.figshare.24912321.v1
title: Ophiura_sarsiiLutken.TRF.gff

description: Repeat sequence annotation of Ophiura sarsii with TRF
DOI: 10.6084/m9.figshare.24912393
title: Appendices (Supplementary material).docx

description: <b>Synergistic Forecasting: A Hybrid SARIMA-LSTM Model for Fatal Crash Prediction in the United States</b>