cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14651011
title: BADIIY-TARIXIY MATERIALLAR VOSITASIDA  BO'LAJAK O'QITUVCHILARNI MA'NAVIY MADANIYATINI RIVOJLANTIRISHNING AHAMIYATI

description: Zamonaviy ta’lim tizimida ma’naviyat, insoniyatning ruhiy va axloqiy o‘sishi uchun eng muhim omillardan biri sifatida qaraladi. Xususan, o‘qituvchilarning ma’naviy madaniyati, nafaqat ularning shaxsiy rivojlanishi, balki ularning jamoaviy hayotini shakllantirishda ham muhim rol o‘ynaydi.  Ushbu maqolada badiiy-tarixiy materiallar yordamida bo‘lajak o‘qituvchilarning ma’naviy madaniyatini rivojlantirishning ahamiyati, asosiy tamoyillari hamda eng samarali usullari haqida so‘z yuritiladi.
DOI: 10.7483/opendata.cms.13op.kh9t
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN10000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.e8zg.x322
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN10000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.e8ai.4nja
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.yfn8.cebx
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fn7q.lu6i
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.upms.tdc5
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.w3c1.v8ik
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.3z8x.fwhs
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ugpy.ilvx
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.uvvv.oh6a
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.6084/m9.figshare.27852665
title: Ethanol for the management of alcohol withdrawal syndrome: a systematic review

description: Alcohol withdrawal is typically managed using benzodiazepines. However, modulation of both γ-aminobutyric acid-A and N-methyl-d-aspartate-receptors through ethanol provision may provide an alternative management strategy. This systematic review critically analyses the evidence surrounding the use of oral or intravenous ethanol for the management of alcohol withdrawal syndrome. Systematic searches of ProQuest – American Psychological Association, PsycInfo, MEDLINE and PubMed Central, Web of Science and Embase were performed (Prospero registration number: CRD42023425224). Search criteria were: Population = Patients receiving pharmacological interventions to treat or prevent alcohol withdrawal in a healthcare setting. Intervention = intravenous or enteral ethanol. Comparator = standard care, benzodiazepines, carbamazepine, adjunct medications including sedatives, or no comparator. Outcomes = complication rates, symptom scores, length of stay in healthcare settings. Exclusions were: preclinical studies, participants less than 18 years old, non-peer reviewed literature, poor study design or poor data quality. Study quality was assessed using an adapted National Institute for Health and Care Research quality tool. A narrative data synthesis approach was adopted. Eight thousand two hundred and four studies were retrieved. Ten were included in the final analysis. Overall study quality was poor. Seven studies reported treatment outcomes that were comparable to a control arm or in which ethanol conferred no detrimental effect. Three studies reported positive outcomes, and one study reported worse outcomes following ethanol administration. The review identified heterogeneity in study design and limited reporting surrounding patient demographics, patient alcohol use history and the practicalities of ethanol administration. As such, implementation of ethanol prescribing for the management of alcohol withdrawal is currently limited due to the quality and translatability of existing data surrounding its use. Further studies are required with more transparent and complete outcome reporting and practical implementation recommendations in order to facilitate the translation of ethanol prescribing for the management of alcohol withdrawal syndrome.
DOI: 10.6084/m9.figshare.27852665.v1
title: Ethanol for the management of alcohol withdrawal syndrome: a systematic review

description: Alcohol withdrawal is typically managed using benzodiazepines. However, modulation of both γ-aminobutyric acid-A and N-methyl-d-aspartate-receptors through ethanol provision may provide an alternative management strategy. This systematic review critically analyses the evidence surrounding the use of oral or intravenous ethanol for the management of alcohol withdrawal syndrome. Systematic searches of ProQuest – American Psychological Association, PsycInfo, MEDLINE and PubMed Central, Web of Science and Embase were performed (Prospero registration number: CRD42023425224). Search criteria were: Population = Patients receiving pharmacological interventions to treat or prevent alcohol withdrawal in a healthcare setting. Intervention = intravenous or enteral ethanol. Comparator = standard care, benzodiazepines, carbamazepine, adjunct medications including sedatives, or no comparator. Outcomes = complication rates, symptom scores, length of stay in healthcare settings. Exclusions were: preclinical studies, participants less than 18 years old, non-peer reviewed literature, poor study design or poor data quality. Study quality was assessed using an adapted National Institute for Health and Care Research quality tool. A narrative data synthesis approach was adopted. Eight thousand two hundred and four studies were retrieved. Ten were included in the final analysis. Overall study quality was poor. Seven studies reported treatment outcomes that were comparable to a control arm or in which ethanol conferred no detrimental effect. Three studies reported positive outcomes, and one study reported worse outcomes following ethanol administration. The review identified heterogeneity in study design and limited reporting surrounding patient demographics, patient alcohol use history and the practicalities of ethanol administration. As such, implementation of ethanol prescribing for the management of alcohol withdrawal is currently limited due to the quality and translatability of existing data surrounding its use. Further studies are required with more transparent and complete outcome reporting and practical implementation recommendations in order to facilitate the translation of ethanol prescribing for the management of alcohol withdrawal syndrome.
DOI: 10.7483/opendata.cms.vmyt.u12o
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.6084/m9.figshare.27999679
title: A prospective study of acute propranolol overdose defining dose thresholds of severe toxicity (ATOM – 9)

description: Propranolol is a beta-adrenoceptor blocking drug with sodium channel-blocking properties that can cause life-threatening toxicity in overdose. Limited research defines dose thresholds of toxicity. We aimed to investigate propranolol overdose and dose thresholds for severe toxicity. This is a prospective series of patients with acute propranolol overdose ≥360 mg from August 2014 to December 2023 enrolled through the Australian TOxicology Monitoring (ATOM) collaboration. Severe toxicity was defined as seizure, coma, inotrope therapy, electrocardiographic evidence of sodium channel blockade, or cardiac arrest. There were 209 presentations in 165 patients (median age 30 years [range 15–80 years]; 117 females, 71%). The median reported dose ingested was 1,000 mg (IQR: 600–2,000 mg; range 360–16,000 mg). Co-ingestion occurred in 155 (74%) patients, most commonly involving benzodiazepines (<i>n</i> = 52). Bradycardia (heart rate &lt;50 beats/min) occurred in 41 (20%), and hypotension (systolic blood pressure &lt;90 mmHg) in 88 (42%). Severe toxicity occurred in 51 patients (24%), with 17 (8%) having a seizure and 29 (14%) developing coma. Forty-one (20%) received inotropes, including 31(15%) who were given epinephrine and 20 (10%) high-dose insulin. Electrocardiographic evidence of sodium channel blockade occurred in 16 (8%). Seven (3%) had a cardiac arrest (reported dose range 2,400–16,000 mg), with two deaths following the ingestion of propranolol 4,000 mg and 16,000 mg. The median length of stay was 17 h (IQR: 11–32 h). In 79 patients who ingested only propranolol, the lowest reported propranolol dose for hypotension was 400 mg and for bradycardia, 800 mg. The lowest reported dose for severe toxicity was propranolol 2,000 mg. In those ingesting propranolol only, 17 of 32 (53%) patients who ingested ≥2,000 mg had severe toxicity. Severe toxicity was common, occurring in a quarter of all propranolol overdoses and half of the isolated propranolol ingestions (≥2,000 mg). The outcome was usually favourable with good supportive care, even in severe toxicity. The dose threshold for severe toxicity in isolated propranolol overdose appeared to be 2,000 mg.
DOI: 10.7483/opendata.cms.7prf.2s3a
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.rei6.1rh4
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN15000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.7clq.1omi
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN15000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yf8o.uwez
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.p0o3.hq1t
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.prij.ls0j
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.kcp5.y4ix
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.mri4.1ear
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.42r5.9jxh
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN1750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.f59w.tyex
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN20000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.odd0.8lch
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN20000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.x4yt.exzd
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4hpx.ck8t
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ijm2.tu4i
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN2500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.51ju.mzc6
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN2500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.q29s.lzcj
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN3000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.d3se.cd49
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN3000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ch9g.ji8w
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN5000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.q8f1.qh4g
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN5000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fb8w.2ab4
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.juk9.de6r
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.lr0k.ip0p
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.t07z.upa9
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.0jxn.frzo
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.q9lh.kwmz
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.wzf5.net2
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN7500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.6084/m9.figshare.28210757
title: Replication Data/Code for Route-based Geocoding of Traffic congestion-Related Social Media Texts on a Complex Network (Manuscript ID IJGIS-2024-1073)

description: <b>Instructions (with screenshots) to replicate results from Section 3 of the manuscript are available in</b><b> "Step-by-step Instructions to Replicate Results.pdf".</b>-------------------------------------------------------------------------------------------------------------------<b>Step 1: </b><b>Download the replication materials</b><br>Download the whole <b>replication </b>folder on figshare containing the code, data and replication files.<br><b>Step 2: Replicate Tables in Section 3</b>All of the data is available inside the sub-folder <b>replication/Data</b>. To replicate Tables 1 and 2 from section 3 of the manuscript run the Python file r<b>eplicate_section3_tables.py locally on your computer</b>. This will produce two .csv files containing Tables 1 and 2 (already provided). Note that it is not necessary to run the code in order to replicate the tables. The output data needed for replication is provided.<b>Step 3: </b><b>Replicate Figures in QGIS</b><br>The Figures must be replicated using QGIS, freely available at https://www.qgis.org/. Open the QGIS project <b>replicate_figures.qgz </b>inside the<b> replication/Replicate Figures </b>sub-folder. It should auto-find the layer data. The<b> </b>Figures are replicated as layers in the project.<b> </b><br><b>Step 4: Running the code from scratch</b><br>The accompanying code for the manuscript IJGIS-2024-1305, entitled "Route-based Geocoding of Traffic Congestion-Related Social Media Texts on a Complex Network" runs on <b>Google Colab as Python notebooks</b>. Please follow the instructions below to run the entire geocoder and network mapper from scratch. The expected running time is of the order of 10 hours on free tier Google Colab. <br><b>4a) Upload to Google Drive</b><br>Upload the entire <b>replication </b>folder to your Google Drive. Note the path (location) to which you have uploaded it. There are two Google Colab notebooks that need to be executed in their entirety. These are Code/Geocoder/The_Geocoder.ipynb and Code/Complex_Network/Complex_network_code.ipynb. They need to be run in order (Geocoder first and Complex Network second). <br><b>4b) Set the path </b><br>In each Google Colab notebook, you have to set the variable called “REPL_PATH” to the location on your Google Drive where you uploaded the replication folder. Include the replication folder in the path. For example "/content/drive/MyDrive/replication"<br><b>4c) Run the code</b><br>The code is available in two sub-folders, <b>replication/Code/Geocoder </b>and <b>replication/Code/Complex_Network</b>. You may simply open the Google Colab notebooks inside each folder, mount your Google Drive, set the path and run all cells. <br><br><br>
DOI: 10.6084/m9.figshare.28210757.v1
title: Replication Data/Code for Route-based Geocoding of Traffic congestion-Related Social Media Texts on a Complex Network (Manuscript ID IJGIS-2024-1073)

description: <b>Instructions (with screenshots) to replicate results from Section 3 of the manuscript are available in</b><b> "Step-by-step Instructions to Replicate Results.pdf".</b>-------------------------------------------------------------------------------------------------------------------<b>Step 1: </b><b>Download the replication materials</b><br>Download the whole <b>replication </b>folder on figshare containing the code, data and replication files.<br><b>Step 2: Replicate Tables in Section 3</b>All of the data is available inside the sub-folder <b>replication/Data</b>. To replicate Tables 1 and 2 from section 3 of the manuscript run the Python file r<b>eplicate_section3_tables.py locally on your computer</b>. This will produce two .csv files containing Tables 1 and 2 (already provided). Note that it is not necessary to run the code in order to replicate the tables. The output data needed for replication is provided.<b>Step 3: </b><b>Replicate Figures in QGIS</b><br>The Figures must be replicated using QGIS, freely available at https://www.qgis.org/. Open the QGIS project <b>replicate_figures.qgz </b>inside the<b> replication/Replicate Figures </b>sub-folder. It should auto-find the layer data. The<b> </b>Figures are replicated as layers in the project.<b> </b><br><b>Step 4: Running the code from scratch</b><br>The accompanying code for the manuscript IJGIS-2024-1305, entitled "Route-based Geocoding of Traffic Congestion-Related Social Media Texts on a Complex Network" runs on <b>Google Colab as Python notebooks</b>. Please follow the instructions below to run the entire geocoder and network mapper from scratch. The expected running time is of the order of 10 hours on free tier Google Colab. <br><b>4a) Upload to Google Drive</b><br>Upload the entire <b>replication </b>folder to your Google Drive. Note the path (location) to which you have uploaded it. There are two Google Colab notebooks that need to be executed in their entirety. These are Code/Geocoder/The_Geocoder.ipynb and Code/Complex_Network/Complex_network_code.ipynb. They need to be run in order (Geocoder first and Complex Network second). <br><b>4b) Set the path </b><br>In each Google Colab notebook, you have to set the variable called “REPL_PATH” to the location on your Google Drive where you uploaded the replication folder. Include the replication folder in the path. For example "/content/drive/MyDrive/replication"<br><b>4c) Run the code</b><br>The code is available in two sub-folders, <b>replication/Code/Geocoder </b>and <b>replication/Code/Complex_Network</b>. You may simply open the Google Colab notebooks inside each folder, mount your Google Drive, set the path and run all cells. <br><br><br>
DOI: 10.7483/opendata.cms.f7in.5r25
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN7500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.99lh.mnt0
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.sqkx.8h46
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.14649380
title: Artifact of "Spectre: Automated Aliasing Specifications Generation for Library APIs with Fuzzing"
DOI: 10.5281/zenodo.14649381
title: Artifact of "Spectre: Automated Aliasing Specifications Generation for Library APIs with Fuzzing"
DOI: 10.7483/opendata.cms.368v.3vqe
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qw13.heol
title: /SSWWTypeIHeavyN-SFmix_Dilepton_NLO_MN800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.kq4ma7
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Erebia alcmena Grum-Grshimailo, 1891"
 ]
}

 The dataset includes 15 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0145690-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tfbmut
title: Occurrence Download

description: A dataset containing 573 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nolina lindheimeriana (Scheele) S.Watson"
 ]
}

 The dataset includes 573 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048380-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.brtepa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is incertae sedis",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((8.57687 39.824715,8.576834 39.823071,8.577113 39.822022,8.577025 39.821602,8.576529 39.82111,8.574542 39.819564,8.574363 39.819213,8.574183 39.818828,8.574187 39.818303,8.573563 39.816376,8.573343 39.815396,8.572613 39.815918,8.568531 39.815798,8.568443 39.815413,8.567354 39.815374,8.567265 39.815164,8.565813 39.815088,8.565724 39.814808,8.564681 39.814839,8.564692 39.81309,8.563331 39.81305,8.563271 39.815324,8.557282 39.815371,8.557234 39.816318,8.549923 39.816778,8.550009 39.817653,8.550365 39.818633,8.550408 39.818984,8.550857 39.81972,8.551303 39.820981,8.5513 39.821366,8.551524 39.821822,8.551835 39.822942,8.551958 39.824972,8.552263 39.826793,8.55235 39.827493,8.552571 39.828298,8.552658 39.828963,8.552791 39.829419,8.552927 39.829489,8.55329 39.829456,8.553878 39.829773,8.553919 39.830368,8.554145 39.830543,8.554281 39.830614,8.554737 39.830196,8.555601 39.829989,8.55601 39.829781,8.556646 39.829818,8.557781 39.829683,8.558189 39.829719,8.559592 39.830354,8.560545 39.830323,8.562541 39.830365,8.563584 39.830474,8.56413 39.830336,8.564539 39.830128,8.565452 39.829361,8.566089 39.829084,8.567588 39.828775,8.568634 39.828359,8.570137 39.827489,8.570729 39.827107,8.571548 39.826795,8.572232 39.826343,8.573051 39.825961,8.573414 39.825857,8.574098 39.825335,8.575915 39.824957,8.57687 39.824715)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030215-230530130749713/datasets/export for details.