cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc26xxdg
title: CCDC 2053576: Experimental Crystal Structure Determination

description: Related Article: Lekai Hou, Lingyan Gao, Wenyao Zhang, Xiao-Juan Yang, Biao Wu|2021|Cryst.Growth Des.|21|2837|doi:10.1021/acs.cgd.1c00034
DOI: 10.6084/m9.figshare.14602059
title: Soil arthropod communities are not affected by invasive Solidago gigantea Aiton (Asteraceae), based on morphology and metabarcoding analyses

description: Dataset for the article: <br>Ustinova, E. N., Schepetov, D. M., Lysenkov, S. N., & Tiunov, A. V. (2021). Soil arthropod communities are not affected by invasive <i>Solidago gigantea</i> Aiton (Asteraceae), based on morphology and metabarcoding analyses. <i>Soil Biology and Biochemistry</i>, V. 159, 108288. https://doi.org/10.1016/j.soilbio.2021.108288<br>Sheet 1. Morphology.<br>Results table of soil arthropods identification by morphological traits. Columns A–D show the taxonomic name, columns E–AW show the number of certain species in the samples. “Solidago” in a sample name means sample from the Solidago gigantea thickets, “Control” means control samples; “Balashikha” – a glade under the transmission line in the city of Balashikha (N 55°47.093′ E 37°55.844′), “Reutov” – an urban wasteland in the city of Reutov (N 55°46.089′ E 37°51.301′), “Lutsino” – a fallow field in Lutsino village in Odintsovsky district (N 55°42.153′ E 36°45.787′).<br>Sheet 2. Morphology families.Results table of soil arthropods families identified by morphological traits. Column A shows the family, columns B–AT show the number of certain families in the samples. Sample names as in sheet 1.<br>List 3. Metabarcoding.Results table of soil arthropods identification by metabarcoding. Column "taxID" means the taxonomic identification number from the taxonomy database, "taxon" – the name of the taxon, "rank" – the rank of the taxon, (e.g. species, genus family, etc), "score" – the Akaike weight from the model selection procedure, columns E–J mean the names of phylum, class, order, family, genus, species; columns K–BC show the number of reads in the samples. Sample names as in sheet 1.<br>List 4. Metabarcoding families.Results table of soil arthropods families identified by metabarcoding. Column A shows the family, columns B–AT show the number of reads in the samples. Sample names as in sheet 1.<br>List 5. Experiment.Results table of soil arthropods identification by morphological traits in the experiment with goldenrod transplantation. Columns A–B show the taxonomic name, columns C–AL show the number of certain species in the samples. “Solidago” in a sample name means sample from the thickets of transplanted Solidago gigantea, “Control” means control samples; “Balashikha” – a summer cottage in in the city of Balashikha (N 55 ° 46.031 ′ E 37 ° 54.234 ′), “Moscow River” – floodplain of the Moscow River in Odintsovsky district (N 55 ° 42.336 ′ E 36 ° 44.843 ′), “Lutsino” – a fallow field in Lutsino village in Odintsovsky district (N 55 ° 42.092 ′ E 36 ° 45.653 ′).
DOI: 10.6084/m9.figshare.14602059.v1
title: Soil arthropod communities are not affected by invasive Solidago gigantea Aiton (Asteraceae), based on morphology and metabarcoding analyses

description: Dataset for the article: <br>Ustinova, E. N., Schepetov, D. M., Lysenkov, S. N., & Tiunov, A. V. (2021). Soil arthropod communities are not affected by invasive <i>Solidago gigantea</i> Aiton (Asteraceae), based on morphology and metabarcoding analyses. <i>Soil Biology and Biochemistry</i>, V. 159, 108288. https://doi.org/10.1016/j.soilbio.2021.108288<br>Sheet 1. Morphology.<br>Results table of soil arthropods identification by morphological traits. Columns A–D show the taxonomic name, columns E–AW show the number of certain species in the samples. “Solidago” in a sample name means sample from the Solidago gigantea thickets, “Control” means control samples; “Balashikha” – a glade under the transmission line in the city of Balashikha (N 55°47.093′ E 37°55.844′), “Reutov” – an urban wasteland in the city of Reutov (N 55°46.089′ E 37°51.301′), “Lutsino” – a fallow field in Lutsino village in Odintsovsky district (N 55°42.153′ E 36°45.787′).<br>Sheet 2. Morphology families.Results table of soil arthropods families identified by morphological traits. Column A shows the family, columns B–AT show the number of certain families in the samples. Sample names as in sheet 1.<br>List 3. Metabarcoding.Results table of soil arthropods identification by metabarcoding. Column "taxID" means the taxonomic identification number from the taxonomy database, "taxon" – the name of the taxon, "rank" – the rank of the taxon, (e.g. species, genus family, etc), "score" – the Akaike weight from the model selection procedure, columns E–J mean the names of phylum, class, order, family, genus, species; columns K–BC show the number of reads in the samples. Sample names as in sheet 1.<br>List 4. Metabarcoding families.Results table of soil arthropods families identified by metabarcoding. Column A shows the family, columns B–AT show the number of reads in the samples. Sample names as in sheet 1.<br>List 5. Experiment.Results table of soil arthropods identification by morphological traits in the experiment with goldenrod transplantation. Columns A–B show the taxonomic name, columns C–AL show the number of certain species in the samples. “Solidago” in a sample name means sample from the thickets of transplanted Solidago gigantea, “Control” means control samples; “Balashikha” – a summer cottage in in the city of Balashikha (N 55 ° 46.031 ′ E 37 ° 54.234 ′), “Moscow River” – floodplain of the Moscow River in Odintsovsky district (N 55 ° 42.336 ′ E 36 ° 44.843 ′), “Lutsino” – a fallow field in Lutsino village in Odintsovsky district (N 55 ° 42.092 ′ E 36 ° 45.653 ′).
DOI: 10.6084/m9.figshare.9099098
title: Tansfusion_data.xlsx

description: transfusionburn
DOI: 10.6084/m9.figshare.9099098.v1
title: Tansfusion_data.xlsx

description: transfusionburn
DOI: 10.6084/m9.figshare.14602095
title: Supplemental Table S1

description: Ranges of mineral contents per each quintile of their distribution. The number of cows per each quintile is reported superscript in parentheses.
DOI: 10.6084/m9.figshare.14602110
title: Supplemental Table S2

description: <b>Supplemental Table S2.</b> Analysis of variance from model M2 (no fat and casein; contemporary inclusion of minerals) for fat, casein, mineral contents, coagulation and cheese-making traits with <i>F</i>-value and significance for fixed factors and the proportion of variance (in percentage) explained by random factors.
DOI: 10.6084/m9.figshare.14602110.v1
title: Supplemental Table S2

description: <b>Supplemental Table S2.</b> Analysis of variance from model M2 (no fat and casein; contemporary inclusion of minerals) for fat, casein, mineral contents, coagulation and cheese-making traits with <i>F</i>-value and significance for fixed factors and the proportion of variance (in percentage) explained by random factors.
DOI: 10.5517/ccdc.csd.cc27kn4m
title: CCDC 2072540: Experimental Crystal Structure Determination

description: Related Article: Rongzhen Xie, Nannan Shen, Xijian Chen, Jie Li, Yaxing Wang, Chao Zhang, Chengliang Xiao, Zhifang Chai, Shuao Wang|2021|Inorg.Chem.|60|6463|doi:10.1021/acs.inorgchem.1c00187
DOI: 10.5517/ccdc.csd.cc26837v
title: CCDC 2033576: Experimental Crystal Structure Determination

description: Related Article: Benny Danilo Belviso, Francesco Marin, Sara Fuertes, Violeta Sicilia, Rosanna Rizzi, Fulvio Ciriaco, Chiara Cappuccino, Eric Dooryhee, Aurelia Falcicchio, Lucia Maini, Angela Altomare, Rocco Caliandro|2021|Inorg.Chem.|60|6349|doi:10.1021/acs.inorgchem.1c00081
DOI: 10.5517/ccdc.csd.cc27b9hd
title: CCDC 2065483: Experimental Crystal Structure Determination

description: Related Article: Mengqing Chen, Wataru Sato, Rui Shang, Eiichi Nakamura|2021|J.Am.Chem.Soc.|143|6823|doi:10.1021/jacs.1c03394
DOI: 10.18112/openneuro.ds003633.v1.0.0
title: ForrestGump-MEG
DOI: 10.6084/m9.figshare.14602185
title: re.model

description: pretrained automodel in KIRE
DOI: 10.6084/m9.figshare.14602185.v1
title: re.model

description: pretrained automodel in KIRE
DOI: 10.5281/zenodo.4763091
title: Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems

description: Los parámetros fisicoquímicos y biológicos correspondientes al LT1 del proyecto Diverfarming.
DOI: 10.5281/zenodo.4763090
title: Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems

description: Los parámetros fisicoquímicos y biológicos correspondientes al LT1 del proyecto Diverfarming.
DOI: 10.25345/c5cv5f
title: MassIVE MSV000087438 - PNT2 Glycan-protein Cross-linking Mass Spectrometry
title: The vast majority of membrane proteins are glycosylated with complicated glycan structures attached to the polypeptide backbone. Glycan-protein interactions are fundamental elements in many cellular events. Therefore, we developed a cross-linking method to elucidate the glycan-mediated interactions between membrane proteins through sialic acids.
DOI: 10.5281/zenodo.4765528
title: Soil physicochemical properties for Diverfarming LT1 case study (diversified vegetable crops in spain)

description: Physicochemical soil properties of the long-term case study LT1 of Diverfarming H2020 project for diversified vegetables in southeast Spain. Includes data of the research article "Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems" ( https://doi.org/10.3390/agriculture11050445)
DOI: 10.5281/zenodo.4765527
title: Soil physicochemical properties for Diverfarming LT1 case study (diversified vegetable crops in spain)

description: Physicochemical soil properties of the long-term case study LT1 of Diverfarming H2020 project for diversified vegetables in southeast Spain. Includes data of the research article "Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems" ( https://doi.org/10.3390/agriculture11050445)
DOI: 10.6084/m9.figshare.14602203
title: Processed data

description: KIRE
DOI: 10.3886/e101880v4-86740
title: Annual Survey of State Government Finances 1992-2018

description: <b>Version 4 release notes:</b>Changes release notes description, does not change data.<b>Version 3 release notes</b><b></b>Adds 2018 data.Renames some columns so all column names are &lt;= 32 characters to fix Stata limit.<br><b>Version 2 release notes</b>Adds 2017 data. R and Stata files now available.<br><br>The .csv file includes data from the years 1992-2016. No data was changed. Only column names were changed to standardize it across years. Some columns (e.g. Population) that are not in all years are removed. <b>Amounts are in thousands of dollars. </b><b><br></b>The zip file includes all raw (completely untouched) files for years 1992-2016. <br><br>From the Census, "The Annual Survey of State Government Finances provides a comprehensive summary of the annual survey findings for state governments, as well as data for individual states. The tables contain detail of revenue by source, expenditure by object and function, indebtedness by term, and assets by purpose." (link to this quote is below)<br><br>Information from the U.S. Census about the data is here. https://www.census.gov/programs-surveys/state/about.html<br>
description: Response Rates: All states have data for all years. In a few years, some values (e.g. expenditure liquor stores) are missing. Overall, almost compete data.<br>
description: State and national government<br>. Smallest Geographic Unit: State and national
DOI: 10.5281/zenodo.4765545
title: GIFdroid: Automated Bug Replay from Visual Bug Report

description: Full list of real world visual bug reports.
DOI: 10.5281/zenodo.4765544
title: GIFdroid: Automated Bug Replay from Visual Bug Report

description: Full list of real world visual bug reports.
DOI: 10.6084/m9.figshare.14602254
title: Hypertriglyceridemia-induced pancreatitis treated with insulin_case series.docx

description: Dataset of 17 patients treated for hyperlipidemia induced acute pancreatitis with continuous insulin infusion for four days with complete recovery.
DOI: 10.6084/m9.figshare.14602254.v1
title: Hypertriglyceridemia-induced pancreatitis treated with insulin_case series.docx

description: Dataset of 17 patients treated for hyperlipidemia induced acute pancreatitis with continuous insulin infusion for four days with complete recovery.
DOI: 10.5281/zenodo.4765564
title: Mass spectrometric and quantum chemical treatments of molecular and ionic interactions of flavonoid-glycosides - a stochastic dynamic approach (SUPPORTING INFORMATION)

description: Quantum chemical <em>ab initio</em> and DFT data on mass spectrometric species at m/z 111, 153 and 579 of a flavonoid-<em>O</em>-glycoside involving frequency analysis of gas- and transition states; atomic coordinates and gas-phase thermochemistry, respectively.
DOI: 10.6084/m9.figshare.14601396
title: Geological features - KML format

description: Geological features of the new geological -structural map of the southern Serre Massif.
DOI: 10.6084/m9.figshare.14601396.v2
title: Geological features - KML format

description: Geological features of the new geological -structural map of the southern Serre Massif.
DOI: 10.6084/m9.figshare.14601396.v1
title: Geological features - KML format

description: Geological features of the new geological -structural map of the southern Serre Massif.
DOI: 10.5281/zenodo.4765568
title: Linked Ogham Ontology

description: Linked Ogham Ontology @ http://ontology.ogham.link
DOI: 10.5281/zenodo.4407344
title: Linked Ogham Ontology

description: Linked Ogham Ontology @ http://ontology.ogham.link
DOI: 10.15161/oar.it/73789
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4765582
title: Outdated (ignore)

description: Outdated (ignore)
DOI: 10.5281/zenodo.4764148
title: Outdated (ignore)

description: Outdated (ignore)
DOI: 10.6084/m9.figshare.14602323
title: SRR5836476_1_val_1.methylation.bigWig

description: data
DOI: 10.6084/m9.figshare.14602323.v1
title: SRR5836476_1_val_1.methylation.bigWig

description: data
DOI: 10.5281/zenodo.4765606
title: Modelling Human-Centric Aspects of End-Users with iStar - dataset

description: User study questionnaire and results
DOI: 10.5281/zenodo.4765605
title: Modelling Human-Centric Aspects of End-Users with iStar - dataset

description: User study questionnaire and results
DOI: 10.25646/8491
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.3886/e121922v1-86760
title: Data and Code for: Social Media, News Consumption, and Polarization: Evidence from a Field Experiment

description: Does the consumption of ideologically congruent news on social media exacerbate polarization? I estimate the effects of social media news exposure by conducting a large field experiment randomly offering participants subscriptions to conservative or liberal news outlets on Facebook. I collect data on the causal chain of media effects: subscriptions to outlets, exposure to news on Facebook, visits to online news sites, and sharing of posts, as well as changes in political opinions and attitudes. Four main findings emerge. First, random variation in exposure to news on social media substantially affects the slant of news sites individuals visit. Second, exposure to counter-attitudinal news decreases negative attitudes toward the opposing political party. Third, in contrast to the effect on attitudes, I find no evidence that the political leanings of news outlets affect political opinions. Fourth, Facebook's algorithm is less likely to supply individuals with posts from counter-attitudinal outlets, conditional on individuals subscribing to them. Together, the results suggest that social media algorithms may limit exposure to counter-attitudinal news and thus increase polarization.<br>
DOI: 10.5281/zenodo.4765597
title: https://www.aclweb.org/anthology/W19-6809/

description: Multilingual Multimodal Machine Translation for Dravidian Languages utilizing Phonetic Transcription @inproceedings{chakravarthi-etal-2019-multilingual,<br> title = "Multilingual Multimodal Machine Translation for {D}ravidian Languages utilizing Phonetic Transcription",<br> author = "Chakravarthi, Bharathi Raja and<br> Priyadharshini, Ruba and<br> Stearns, Bernardo and<br> Jayapal, Arun and<br> S, Sridevy and<br> Arcan, Mihael and<br> Zarrouk, Manel and<br> McCrae, John P",<br> booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",<br> month = aug,<br> year = "2019",<br> address = "Dublin, Ireland",<br> publisher = "European Association for Machine Translation",<br> url = "https://www.aclweb.org/anthology/W19-6809",<br> pages = "56--63",<br> }
DOI: 10.5281/zenodo.4765596
title: https://www.aclweb.org/anthology/W19-6809/

description: Multilingual Multimodal Machine Translation for Dravidian Languages utilizing Phonetic Transcription @inproceedings{chakravarthi-etal-2019-multilingual,<br> title = "Multilingual Multimodal Machine Translation for {D}ravidian Languages utilizing Phonetic Transcription",<br> author = "Chakravarthi, Bharathi Raja and<br> Priyadharshini, Ruba and<br> Stearns, Bernardo and<br> Jayapal, Arun and<br> S, Sridevy and<br> Arcan, Mihael and<br> Zarrouk, Manel and<br> McCrae, John P",<br> booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",<br> month = aug,<br> year = "2019",<br> address = "Dublin, Ireland",<br> publisher = "European Association for Machine Translation",<br> url = "https://www.aclweb.org/anthology/W19-6809",<br> pages = "56--63",<br> }
DOI: 10.6084/m9.figshare.9821201
title: Aging Cell Data

description: Thirteen human aging data set + 6 negative non aging data set
DOI: 10.6084/m9.figshare.9821201.v2
title: Aging Cell Data

description: Thirteen human aging data set + 6 negative non aging data set
DOI: 10.6084/m9.figshare.9821201.v1
title: Aging Cell Data

description: Thirteen human aging data set + 6 negative non aging data set
DOI: 10.5281/zenodo.4736109
title: Luftdaten.Info data set 2015 - 5min, 30min values

description: The data set contains the normalized averaged 5 and 30 minutes value of the Sensor.Community - archives, formerly Luftdaten.info, of the year 2015. The tar-archive includes the CSV-files, XZ-compressed. For each sensor and day, the averaged values are available and each CSV-file contains a header. For particulate matter data (sensors: PPD42NS):<br> <em>timestamp,sensor_id,location,lat,lon,P1,durP1,ratioP1,P2,durP2,ratioP2</em> Description: timestamp: Date and Time of measurement sensor_id: ID of Sensor lat: Latitude lon: Longitude P1: value of Particle Matter 10µg/m³ (PM10) durP1: ratioP1: P2: value of Particle Matter 2.5µg/m³ (PM2.5) durP2: ratioP2:
DOI: 10.5281/zenodo.4736108
title: Luftdaten.Info data set 2015 - 5min, 30min values

description: The data set contains the normalized averaged 5 and 30 minutes value of the Sensor.Community - archives, formerly Luftdaten.info, of the year 2015. The tar-archive includes the CSV-files, XZ-compressed. For each sensor and day, the averaged values are available and each CSV-file contains a header. For particulate matter data (sensors: PPD42NS):<br> <em>timestamp,sensor_id,location,lat,lon,P1,durP1,ratioP1,P2,durP2,ratioP2</em> Description: timestamp: Date and Time of measurement sensor_id: ID of Sensor lat: Latitude lon: Longitude P1: value of Particle Matter 10µg/m³ (PM10) durP1: ratioP1: P2: value of Particle Matter 2.5µg/m³ (PM2.5) durP2: ratioP2:
DOI: 10.5281/zenodo.4559837
title: Deaf participants' elaborations on the extended list of their preferred health-information sources

description: These elaborations were derived from three groups of Deaf participants: single Deaf men, single Deaf women, and Deaf families who participated Co-exploration Activity 1.
DOI: 10.5281/zenodo.4559836
title: Deaf participants' elaborations on the extended list of their preferred health-information sources

description: These elaborations were derived from three groups of Deaf participants: single Deaf men, single Deaf women, and Deaf families who participated Co-exploration Activity 1.
DOI: 10.6084/m9.figshare.14330795.v3
title: An Immune Cell Atlas Reveals Dynamic COVID-19 Specific Neutrophil Programming Amendable to Therapy

description: SARS-CoV-2 is a novel coronavirus that causes acute respiratory distress syndrome (ARDS) and death. Innate immune cells are critical for host defense but are also the primary drivers of acute respiratory distress syndrome. The relationships between innate cellular responses in ARDS resulting from COVID-19 compared to other causes of ARDS, such as bacterial sepsis is unclear. Moreover, the beneficial cellular effects of dexamethasone therapy during severe COVID-19 remains speculative but understanding their mechanistic effects could improve rationally targeted drug design. We discovered that compared to bacterial septic ARDS, COVID-19 induced distinct neutrophil polarization characterized by either interferon (IFN) or prostaglandin (PG) active states. IFN polarization required the transcriptional regulators PRDM1, STAT1 and IRF1 while prostaglandin polarization was induced by E2F4. Bacterial ARDS neutrophils upregulated antibacterial molecules such as PLAC8 via STAT5b and demonstrated conventional transcription factors CEBPA and CEBPB. Steroid therapy rapidly altered IFN polarization, downregulated interferon responsive genes, and induced immunoretrained neutrophils withelevations of the decoy IL-1R2 regulatory receptor. Steroids induced the emergence of immature neutrophils expressing immunosuppressive molecules ARG1 and ANXA1. Moreover, steroids remodeled global cellular communication hierarchies by changing neutrophils from information receivers into information providers. Importantly, male patients had higher proportions of IFN-active neutrophils and a greater degree of steroid induced attenuation of cellular polarization. Indeed, the highest proportion of IFN-active neutrophils was associated with mortality. These results define neutrophil states unique to COVID-19 when contextualized to other life-threatening infections, thereby enhancing the relevance of our findings at the bedside. Furthermore, the clinical benefits of dexamethasone therapy are molecularly defined, and this information highlights essential molecular pathways to which improved therapeutic targeting can now be conceived.<br>