cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.21724223.v1
title: The PERUSANO study: Caregivers, health centre and day care centre influences on infant and young child feeding practices: dataset for health facilities

description: Qualitative data from semi-structured in-depth interviews, exit interviews and observations were collected as part of a wider interdisciplinary project (PERUSANO) aimed at addressing multiple forms of malnutrition, particularly stunting, anaemia, and risk of overweight/obesity in peri-urban Peruvian infants and young children (IYC) aged 6-23 months of age. The study took place in two peri-urban areas: Manchay in Lima and Huánuco district in the Andean highlands. <br> Interviews and observations explored drivers of infant and young child complementary feeding (IYCF) practices from the perspective of primary caregivers within the home environment, health professionals involved in CRED care (well-baby checks, nutrition counselling) and staff involved in the CUNAMAS National Programme in day care services. The CUNAMAS is a large-scale early child development programme established by the Ministry of Development and Social Inclusion in 2012. It aims to support the holistic development of children under three living in poverty. <br> Data collection in four health centres and fourteen CUNAMAS day care services took place between October and December 2019. Participants/key informants were purposefully selected to cover the main roles within the health facilities (health professionals involved in CRED care, nutritionists, primary caregivers of children), and CUNAMAS day care services (nursery caregivers, district nursery technical managers, mother guides, and primary caregivers of children). <br> Interviews and observations with primary caregivers took place between January and mid-March 2020. A purposive quota sampling was employed to recruit mothers according to the child’s age, maternal working status and nutritional status from participants who had taken part in the quantitative survey of the PERUSANO project. In March 2020, face-to-face interviews stopped due to the COVID-19 pandemic and the remaining interviews with caregivers were conducted by telephone. Observations could no longer be conducted. <br> Transcripts of interviews and observations in Spanish language are available upon request to the UK Principal Investigator Professor Emily Rousham (e.k.rousham@lboro.ac.uk). Detailed sampling information according to interview type, location and stakeholder group can be found in Supplementary material 1. See also the README_HP file attached. <br> The quantitative component of the study has been described elsewhere (Pradeilles et al. 2022). Quantitative survey questionnaires and data can be found at 10.17028/rd.lboro.18750458, 10.17028/rd.lboro.16691455 and 10.17028/rd.lboro.21656348. <br> <br> <br>
DOI: 10.5281/zenodo.7437551
title: pionites_melanocephalus_decontaminated_FSCR.fasta,gz

description: .
DOI: 10.5281/zenodo.7437550
title: pionites_melanocephalus_decontaminated_FSCR.fasta,gz

description: .
DOI: 10.17632/pttvxjcmpd.1
title: SyntheticHumanModel_Male_Part5

description: Part 5 of a set of 60 Synthetic Virtual Human Male Models in fbx formats. Each model has mesh, skeletons and textures associated with the models.
DOI: 10.17632/pttvxjcmpd.2
title: SyntheticHeadPoseDataset_Female_Part2

description: Part 2 of the synthetic facial data rendered from female fbx models with real background. The total dataset contains around 40k facial images generated from 12 identity and the corresponding head pose.
DOI: 10.18728/igb-fred-807.0
title: Datenbank Projekt BAGGERSEE 2016-2022

description: The database is based on a table with general information about the lakes (lake type, morphometric information etc.). At each lake water parameters (e.g. pH value, Total phosphorus etc) were measured at nearly every sampling event. Sampling events comprise several types of fishing events (various types of electrofishing and gillnetting) and sampling events for other biological taxa such as submerged macrophytes, birds, crayfish, amphibians, Macrozoobenthos, shoreline vegetation. Furthermore, deadwood and garbage were sampled at the lakes. For the lakes manipulated in the Project (deadwood addition, shallow zones, fish stocking) additional information about the treatment measures are available is specific tables. Each table contains a description of the columns.
DOI: 10.34740/kaggle/dsv/4722340
title: COVID Vaccination in World (updated daily)
DOI: 10.25403/upresearchdata.21376992
title: Effect of development in different host fruit on the body condition, wing morphology and fligh capacity of Bactrocera dorsalis

description: This dataset contains tables detailing the effect of immature development in different host fruit, landmark measurements, wing morphology, flight muscel mass, lipid content and flight capacity of <em>B. dorsalis </em>based on laboratory flight recordings. In order to open this data, RStudio and MorphoJ software can be used.
DOI: 10.25403/upresearchdata.21376992.v1
title: Effect of development in different host fruit on the body condition, wing morphology and fligh capacity of Bactrocera dorsalis

description: This dataset contains tables detailing the effect of immature development in different host fruit, landmark measurements, wing morphology, flight muscel mass, lipid content and flight capacity of <em>B. dorsalis </em>based on laboratory flight recordings. In order to open this data, RStudio and MorphoJ software can be used.
DOI: 10.6084/m9.figshare.21724325
title: Supplementary Material for: Profiles of Successful Aging and Their Association with All-Cause Mortality Risk among the Oldest-Old Chinese in Singapore

description: <b><i>Introduction:</i></b> Although successful aging (SA) studies have examined objective indicators such as disease and disability, physical and cognitive function, and social and productive engagement, as well as subjective indicators such as self-rated health, function, and well-being, the interplay among these indicators is rarely studied. We studied SA profiles that captured this interplay and evaluated the association of these profiles with mortality in the oldest-old. <b><i>Methods:</i></b> Respondents were 1,000 Chinese Singaporeans aged ≥85 years during interview visits from 2017 to 2018. Latent class analysis examined 12 objective and subjective indicators to identify SA profiles. Multivariable Cox regression assessed the relationship between these profiles and all-cause mortality risk through 2020. <b><i>Results:</i></b> Four distinct SA profiles were identified: “frail and dejected” (poor performance in nearly all objective and subjective indicators), “frail but resilient” (poor in objective but good in subjective indicators), “fairly fit and neutral” (good in about half of the indicators), and “fit and positive” (good in nearly all indicators). Compared with “frail and dejected,” the adjusted hazard ratio (95% confidence interval) for mortality risk was 0.63 (0.40–0.97) in “frail but resilient,” 0.56 (0.34–0.93) in “fairly fit and neutral,” and 0.31 (0.19–0.49) in “fit and positive.” <b><i>Discussion:</i></b> SA in the oldest-old could take different profiles based on objective and subjective indicators, and these profiles have implications for mortality risk. Individuals with good subjective indicators have advantage in survival despite poor objective indicators.
DOI: 10.6084/m9.figshare.21724325.v1
title: Supplementary Material for: Profiles of Successful Aging and Their Association with All-Cause Mortality Risk among the Oldest-Old Chinese in Singapore

description: <b><i>Introduction:</i></b> Although successful aging (SA) studies have examined objective indicators such as disease and disability, physical and cognitive function, and social and productive engagement, as well as subjective indicators such as self-rated health, function, and well-being, the interplay among these indicators is rarely studied. We studied SA profiles that captured this interplay and evaluated the association of these profiles with mortality in the oldest-old. <b><i>Methods:</i></b> Respondents were 1,000 Chinese Singaporeans aged ≥85 years during interview visits from 2017 to 2018. Latent class analysis examined 12 objective and subjective indicators to identify SA profiles. Multivariable Cox regression assessed the relationship between these profiles and all-cause mortality risk through 2020. <b><i>Results:</i></b> Four distinct SA profiles were identified: “frail and dejected” (poor performance in nearly all objective and subjective indicators), “frail but resilient” (poor in objective but good in subjective indicators), “fairly fit and neutral” (good in about half of the indicators), and “fit and positive” (good in nearly all indicators). Compared with “frail and dejected,” the adjusted hazard ratio (95% confidence interval) for mortality risk was 0.63 (0.40–0.97) in “frail but resilient,” 0.56 (0.34–0.93) in “fairly fit and neutral,” and 0.31 (0.19–0.49) in “fit and positive.” <b><i>Discussion:</i></b> SA in the oldest-old could take different profiles based on objective and subjective indicators, and these profiles have implications for mortality risk. Individuals with good subjective indicators have advantage in survival despite poor objective indicators.
DOI: 10.6084/m9.figshare.21444864
title: Supplementary Material for: Association between plasma xanthine oxidoreductase activity and the renal function in a general Japanese population: The Tohoku Medical Megabank community-based cohort study

description: Introduction: Xanthine oxidoreductase (XOR) has been identified as a critical source of reactive oxygen species in various pathophysiological conditions, including hypertension, endothelial dysfunction and atherosclerosis. This study investigated the association between XOR and renal function in a general Japanese population. Methods: The Iwate Tohoku Medical Megabank Organization pooled individual participant data from a community-based cohort study in Iwate prefecture. Chronic kidney disease (CKD) was estimated using the estimated glomerular filtration rate of cystatin C (eGFRcys). Individuals with a history of hyperuricemia or severe renal dysfunction (eGFRcys &lt; 15 ml/min/1.73 m2 or undergoing dialysis) were excluded from the study. We performed a multinominal multivariate logistic analysis adjusted for age, blood pressure, uric acid, glycated hemoglobin A1c, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol to associate XOR activity and renal function. Results: The present study included 4,248 participants (male/female: 1,373/2,875, age: 62.9 ± 11.7 years). When participants were divided according to XOR quartiles, blood pressure, body mass index, uric acid, low-density lipoprotein cholesterol and glycated hemoglobin A1c were highest in the highest XOR quartile (all p &lt; 0.001). The XOR activity was significantly higher in the subgroup with CKD stage G3 and G4 (G1 vs. G2 vs. G3-G4: 44.8 ± 40.5 vs 52.0 ± 42.9 vs. 54.1 ± 43.9 pmol / h / mL, p = 0.02). The higher XOR activity was significantly associated with an increase of CKD stage: the odd ratios (95% confidence intervals) per 1 pmol/h/mL increase in XOR activity with CKD stage G1 as a reference were 1.37 (1.13-1.73) in G2 and 1.51 (1.30-1.84) in G3-G4. Conclusion: The present study concluded that high XOR activity was associated with the severity of CKD in a general Japanese population, suggesting that upregulated XOR activity may be involved in advanced renal dysfunction.
DOI: 10.6084/m9.figshare.21444864.v1
title: Supplementary Material for: Association between plasma xanthine oxidoreductase activity and the renal function in a general Japanese population: The Tohoku Medical Megabank community-based cohort study

description: Introduction: Xanthine oxidoreductase (XOR) has been identified as a critical source of reactive oxygen species in various pathophysiological conditions, including hypertension, endothelial dysfunction and atherosclerosis. This study investigated the association between XOR and renal function in a general Japanese population. Methods: The Iwate Tohoku Medical Megabank Organization pooled individual participant data from a community-based cohort study in Iwate prefecture. Chronic kidney disease (CKD) was estimated using the estimated glomerular filtration rate of cystatin C (eGFRcys). Individuals with a history of hyperuricemia or severe renal dysfunction (eGFRcys &lt; 15 ml/min/1.73 m2 or undergoing dialysis) were excluded from the study. We performed a multinominal multivariate logistic analysis adjusted for age, blood pressure, uric acid, glycated hemoglobin A1c, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol to associate XOR activity and renal function. Results: The present study included 4,248 participants (male/female: 1,373/2,875, age: 62.9 ± 11.7 years). When participants were divided according to XOR quartiles, blood pressure, body mass index, uric acid, low-density lipoprotein cholesterol and glycated hemoglobin A1c were highest in the highest XOR quartile (all p &lt; 0.001). The XOR activity was significantly higher in the subgroup with CKD stage G3 and G4 (G1 vs. G2 vs. G3-G4: 44.8 ± 40.5 vs 52.0 ± 42.9 vs. 54.1 ± 43.9 pmol / h / mL, p = 0.02). The higher XOR activity was significantly associated with an increase of CKD stage: the odd ratios (95% confidence intervals) per 1 pmol/h/mL increase in XOR activity with CKD stage G1 as a reference were 1.37 (1.13-1.73) in G2 and 1.51 (1.30-1.84) in G3-G4. Conclusion: The present study concluded that high XOR activity was associated with the severity of CKD in a general Japanese population, suggesting that upregulated XOR activity may be involved in advanced renal dysfunction.
DOI: 10.5281/zenodo.7437644
title: Mass spectrometry- based biomarkers to detect prostate cancer: A multicentric study based on non- invasive urine collection without prior intervention

description: These are datasets related to mass spectrometry proteomics analysis, in particular Capillary Electrophoresis coupled to Mass Spectrometery (CE-MS). CE-MS was employed to acquire proteomics profiles from 970 patients from two different clinical centers. with the aim to develop a biomarker model to detect Prostate Cancer.
DOI: 10.5281/zenodo.7158327
title: Transaction Graph Dataset for the Bitcoin Blockchain - Part 4 of 4

description: This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Part1 is available at https://zenodo.org/deposit/7157356<br> Part2 is available at https://zenodo.org/deposit/7157854<br> Part3 is available at https://zenodo.org/deposit/7158133 Details of the datasets are given below: <strong>FILENAME FORMAT:</strong> The filenames have the following format: btc-tx-&lt;start blockno&gt;-&lt;end blockno&gt;-&lt;part&gt;.bz2 where &lt;start blockno&gt; is the starting block number, &lt;end blockno&gt; final block number, and &lt;part&gt; is the split part of the file. For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from block 100000 to block 149999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. <strong>TRANSACTION FORMAT:</strong> Each line in a file corresponds to a transaction. The transaction has the following format: &lt;SYMBOL&gt; &lt;blockno&gt; &lt;txno&gt; &lt;from&gt; &lt;to&gt; &lt;value&gt; <br> &lt;SYMBOL&gt; Type of transaction (i.e. BTC-IN or BTC-OUT). &lt;blockno&gt; Number of the block which contains the transaction. &lt;txno&gt; Position of the transaction in the block (i.e. transaction number in the block). &lt;from&gt; Source bitcoin address/transaction of the transfer. &lt;to&gt; Destination bitcoin address/transaction of the transfer. &lt;value&gt; Amount of transfer. <strong>BLOCK TIME FORMAT:</strong> The block time file has the following format: &lt;block no&gt; &lt;timestamp&gt; <br> &lt;block no&gt; Number of the block. &lt;timestamp&gt; Unix timestamp at which the block is mined as a hexadecimal number. <strong>IMPORTANT NOTE:</strong> Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. <strong>NOTE:</strong> If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14 <pre><code>@incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {\"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }</code></pre>
DOI: 10.5281/zenodo.7158328
title: Transaction Graph Dataset for the Bitcoin Blockchain - Part 4 of 4

description: This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Part1 is available at https://zenodo.org/deposit/7157356<br> Part2 is available at https://zenodo.org/deposit/7157854<br> Part3 is available at https://zenodo.org/deposit/7158133 Details of the datasets are given below: <strong>FILENAME FORMAT:</strong> The filenames have the following format: btc-tx-&lt;start blockno&gt;-&lt;end blockno&gt;-&lt;part&gt;.bz2 where &lt;start blockno&gt; is the starting block number, &lt;end blockno&gt; final block number, and &lt;part&gt; is the split part of the file. For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from block 100000 to block 149999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. <strong>TRANSACTION FORMAT:</strong> Each line in a file corresponds to a transaction. The transaction has the following format: &lt;SYMBOL&gt; &lt;blockno&gt; &lt;txno&gt; &lt;from&gt; &lt;to&gt; &lt;value&gt; <br> &lt;SYMBOL&gt; Type of transaction (i.e. BTC-IN or BTC-OUT). &lt;blockno&gt; Number of the block which contains the transaction. &lt;txno&gt; Position of the transaction in the block (i.e. transaction number in the block). &lt;from&gt; Source bitcoin address/transaction of the transfer. &lt;to&gt; Destination bitcoin address/transaction of the transfer. &lt;value&gt; Amount of transfer. <strong>BLOCK TIME FORMAT:</strong> The block time file has the following format: &lt;block no&gt; &lt;timestamp&gt; <br> &lt;block no&gt; Number of the block. &lt;timestamp&gt; Unix timestamp at which the block is mined as a hexadecimal number. <strong>IMPORTANT NOTE:</strong> Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. <strong>NOTE:</strong> If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14 <pre><code>@incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {\"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }</code></pre>
DOI: 10.5281/zenodo.7158133
title: Transaction Graph Dataset for the Bitcoin Blockchain - Part 3 of 4

description: This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Part1 is available at https://zenodo.org/deposit/7157356<br> Part2 is available at https://zenodo.org/deposit/7157854<br> Part4 is available at https://zenodo.org/deposit/7158328 Details of the datasets are given below: <strong>FILENAME FORMAT:</strong> The filenames have the following format: btc-tx-&lt;start blockno&gt;-&lt;end blockno&gt;-&lt;part&gt;.bz2 where &lt;start blockno&gt; is the starting block number, &lt;end blockno&gt; final block number, and &lt;part&gt; is the split part of the file. For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from block 100000 to block 149999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. <strong>TRANSACTION FORMAT:</strong> Each line in a file corresponds to a transaction. The transaction has the following format: &lt;SYMBOL&gt; &lt;blockno&gt; &lt;txno&gt; &lt;from&gt; &lt;to&gt; &lt;value&gt; <br> &lt;SYMBOL&gt; Type of transaction (i.e. BTC-IN or BTC-OUT). &lt;blockno&gt; Number of the block which contains the transaction. &lt;txno&gt; Position of the transaction in the block (i.e. transaction number in the block). &lt;from&gt; Source bitcoin address/transaction of the transfer. &lt;to&gt; Destination bitcoin address/transaction of the transfer. &lt;value&gt; Amount of transfer. <strong>BLOCK TIME FORMAT:</strong> The block time file has the following format: &lt;block no&gt; &lt;timestamp&gt; <br> &lt;block no&gt; Number of the block. &lt;timestamp&gt; Unix timestamp at which the block is mined as a hexadecimal number. <strong>IMPORTANT NOTE:</strong> Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. <strong>NOTE:</strong> If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14 <pre><code>@incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {\"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }</code></pre>