cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/aokmhk/u1ozvd
title: 14638_Z02_HR1_Plot_MeteoData 2019 (1)_2.1.1.html
DOI: 10.25625/0zg4ax/rsbe1f
title: 14633_Z02_HF3_Plot_MeteoData 2019 (1)_1.0.0.xml
DOI: 10.25625/0nl2d7/ifu6lm
title: 14634_Z02_HF4_Plot_MeteoData 2019 (1)_1.1.0.html
DOI: 10.25625/cbipa7/flmrzr
title: 14639_Z02_HR2_Plot_MeteoData 2019 (1)_1.1.0.xml
DOI: 10.25625/jvashe/2hauot
title: 14884.xlsx
DOI: 10.25625/1qkkkq/lay3ey
title: 14635_Z02_HO2_Plot_MeteoData 2019 (1)_1.1.1.xml
DOI: 10.25625/ql6kwv/9quefq
title: 14889_Z02_BO3_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/eck2ef/ncdvde
title: 14636.xlsx
DOI: 10.25625/gjv6ol/akgu7t
title: 14885.xlsx
DOI: 10.25625/kni5mn/xg3oqo
title: 14890_Z02_BO4_Plot_MeteoData 2019 (2)_1.1.2.xml
DOI: 10.25625/me41f9/rryiyf
title: 14641.xls
DOI: 10.25625/0zg4ax
title: Z02_HF3_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/3mw2gi/ksc9en
title: 14640.xlsx
DOI: 10.25625/iq7iqj/abuchx
title: 14637_Z02_HO4_Plot_MeteoData 2019 (1)_1.1.0.html
DOI: 10.25625/yix1tl/x5uqjy
title: 14887.xlsx
DOI: 10.25625/aokmhk/rhioom
title: 14638.xlsx
DOI: 10.25625/0nl2d7
title: Z02_HF4_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/oflcgl/vfvupn
title: 14886.xlsx
DOI: 10.25625/1qkkkq
title: Z02_HO2_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/74dphg/7kbvz9
title: 14888_Z02_BO2_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/jvashe/wsf59u
title: 14884_Z02_BF1_Plot_MeteoData 2019 (2)_1.1.0.xml
DOI: 10.25625/ql6kwv/vlmm1i
title: 14889_Z02_BO3_Plot_MeteoData 2019 (2)_1.1.0.xml
DOI: 10.25625/cbipa7/ssgzi9
title: 14639_Z02_HR2_Plot_MeteoData 2019 (1)_1.1.0.html
DOI: 10.25625/eck2ef
title: Z02_HO3_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/me41f9/mus3ws
title: 14641_Z02_HR4_Plot_MeteoData 2019 (1)_1.1.0.html
DOI: 10.25625/gjv6ol/s3o00n
title: 14885_Z02_BF2_Plot_MeteoData 2019 (2)_1.1.0.xml
DOI: 10.25625/iq7iqj
title: Z02_HO4_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/3mw2gi/abdqti
title: 14640_Z02_HR3_Plot_MeteoData 2019 (1)_1.1.0.html
DOI: 10.25625/aokmhk
title: Z02_HR1_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/kni5mn/p0ze6r
title: 14890_Z02_BO4_Plot_MeteoData 2019 (2)_1.1.2.html
DOI: 10.25625/me41f9
title: Z02_HR4_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/yix1tl/ckankh
title: 14887_Z02_BF4_Plot_MeteoData 2019 (2)_1.1.2.html
DOI: 10.25625/kmmzhq/cj2ion
title: 14891_Z02_BR1_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/cbipa7
title: Z02_HR2_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/jvashe
title: Z02_BF1_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/3nzlgx/zncagr
title: 14892.xlsx
DOI: 10.25625/74dphg/mk07nd
title: 14888_Z02_BO2_Plot_MeteoData 2019 (2)_1.1.0.xml
DOI: 10.25625/jlcfeg/videxu
title: 14894_Z02_BR4_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/ql6kwv/nfj09e
title: 14889.xlsx
DOI: 10.25625/yix1tl
title: Z02_BF4_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/3mw2gi
title: Z02_HR3_Plot_MeteoData 2019 (1)

description: quality flag internal temperature
DOI: 10.25625/oflcgl/tau9cf
title: 14886_Z02_BF3_Plot_MeteoData 2019 (2)_2.1.1.html
DOI: 10.25625/gjv6ol
title: Z02_BF2_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/xkdnpw/9twjqf
title: 14893_Z02_BR3_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/kni5mn/qpd5is
title: 14890.xlsx
DOI: 10.25625/loocxw/ttkamp
title: 14895.xlsx
DOI: 10.25625/74dphg
title: Z02_BO2_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/kmmzhq/a7grox
title: 14891.xlsx
DOI: 10.25625/tfwuvh/wnlwzn
title: 14896.xlsx
DOI: 10.25625/2ew74l/y3u4qd
title: 14897_Z02_HF3_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/oflcgl
title: Z02_BF3_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/ql6kwv
title: Z02_BO3_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/ylcek7/6ki6xr
title: 14898_Z02_HF4_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/3nzlgx/uyxif7
title: 14892_Z02_BR2_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/hbxuuj/adjzn2
title: 14899_Z02_HO2_Plot_MeteoData 2019 (2)_1.1.0.html
DOI: 10.25625/kni5mn
title: Z02_BO4_Plot_MeteoData 2019 (2)

description: quality flag internal temperature
DOI: 10.25625/jlcfeg/zfvfvl
title: 14894.xlsx
DOI: 10.25625/ti4whx/fpkifp
title: 14901_Z02_HO4_Plot_MeteoData 2019 (2)_1.1.0.xml
DOI: 10.25625/x0fue1/g3ochr
title: 14900_Z02_HO3_Plot_MeteoData 2019 (2)_1.1.0.xml
DOI: 10.6084/m9.figshare.23310329
title: Publications of International Organizations on Educational Eigitalization

description: List of (policy) documents of international organizations (IOs) on digitalization in education
DOI: 10.6084/m9.figshare.23310329.v1
title: Publications of International Organizations on Educational Eigitalization

description: List of (policy) documents of international organizations (IOs) on digitalization in education
DOI: 10.6084/m9.figshare.23232263.v1
title: Model input files and focal mechanism information

description: simple fault models.zip and complex fault models.zip contain input Poly3D and Triinv files. SoCal_focal_mechanisms.mat contains information about the focal mechanisms we use to derive stress states using the MSATSI software (Martinez-Garzon et al., 2014). file explanation.pdf provides detailed information about each file and its contents. The input files in this database run with Poly3D or triinv. Poly3d is made available by the Stanford Tectonic Geomorphology lab at https://github.com/stgl/poly3d. The inversion software Triinv is available at https://github.com/jploveless/triinv.
DOI: 10.6084/m9.figshare.23312591
title: Fernández-Melero et al. 2023 - Raw Data.xlsx

description: SNP genotyping data from sunflower broomrape populations collected in Cuenca province, central Spain, and classical populations of the Guadalquivir Valley and Cuenca areas used as controls.
DOI: 10.6084/m9.figshare.23312591.v1
title: Fernández-Melero et al. 2023 - Raw Data.xlsx

description: SNP genotyping data from sunflower broomrape populations collected in Cuenca province, central Spain, and classical populations of the Guadalquivir Valley and Cuenca areas used as controls.
DOI: 10.6084/m9.figshare.23310326
title: <strong>Data information collected from the cement clinker production process</strong>

description: This data set is based on one year of cement kiln operation. It includes specific standard coal consumption, feeding quantity of raw meal, coal at kiln inlet and coal at kiln outlet. It excludes some abnormal operating points.
DOI: 10.6084/m9.figshare.23312522
title: A Chromosome scale assembly of the tarnished plant bug, <em>Lygus lineolaris</em> (Palisot de Beauvois), genome. Supplementary Data Tables S1, S2, and S3.

description: Supplementary Data Table S1. BUSCO completeness statistics for the Lygus lineolaris genome assembly with Arthropoda and Hemiptera marker set. BUSCO version 5.2.2 was used to generate these statistics <br> Supplementary Data Table S2: The number of repeat units, the total length and the percentage of different repeat families identified in Piezodorus guildinii genome. The query species was assumed to be Insecta. RepeatMasker version 4.1.2-p1 was run in sensitive mode with rmblastn version 2.11.0+ and FamDB: CONS-Dfam_3.4. <br> Supplementary Data Table S3. Single Nucleotide Polymorphism (SNP) markersmanually selected from the 18 largest scaffolds of Lygus lineolaris genome assembly to develop a SNP typing panel. The number after SCF represents the scaffold number and the the number after the hyphan is an arbitrary sequences number assigned to potential SNPs during manual selection.
DOI: 10.6084/m9.figshare.23312522.v1
title: A Chromosome scale assembly of the tarnished plant bug, <em>Lygus lineolaris</em> (Palisot de Beauvois), genome. Supplementary Data Tables S1, S2, and S3.

description: Supplementary Data Table S1. BUSCO completeness statistics for the Lygus lineolaris genome assembly with Arthropoda and Hemiptera marker set. BUSCO version 5.2.2 was used to generate these statistics <br> Supplementary Data Table S2: The number of repeat units, the total length and the percentage of different repeat families identified in Piezodorus guildinii genome. The query species was assumed to be Insecta. RepeatMasker version 4.1.2-p1 was run in sensitive mode with rmblastn version 2.11.0+ and FamDB: CONS-Dfam_3.4. <br> Supplementary Data Table S3. Single Nucleotide Polymorphism (SNP) markersmanually selected from the 18 largest scaffolds of Lygus lineolaris genome assembly to develop a SNP typing panel. The number after SCF represents the scaffold number and the the number after the hyphan is an arbitrary sequences number assigned to potential SNPs during manual selection.
DOI: 10.6084/m9.figshare.21791975.v1
title: The study of Veraspe variegatus W chromosome provides valuable insights into the evolution of sex chromosomes in flatfish and sheds light on the important role of whole-genome duplication in shaping the evolution of sex chromosomes.

description: The annotation files of the Veraspe W chromosomer variegatus
DOI: 10.21227/7129-9b09
title: Supplementary Material for  “An Efficient Data-Driven Conditional  Joint Wind Power Scenario Generation for  Day-Ahead Power System Operations Planning” 

description: To ensure brevity, the main manuscript provides an abridged version of the scenario evaluation methodology and results. First, this supplementary material provides a discussion of the DM-test procedure employed and its validity. A detailed discussion and specific references related to the procedure applied to test the statistical significance in the improvement of the multivariate scores are presented. Then, the CC-ERCO formulation details, related SG evaluation procedure, and additional results are presented and outlined in more detail, supplementing the main article. The univariate evaluation metric results of sharpness and calibration are also provided. Finally, the time and memory requirement of the proposed SG method in comparison to the benchmark methods is discussed.
DOI: 10.5519/qd.smt2kibw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (7 records)
DOI: 10.6084/m9.figshare.23301326
title: Supplementary Material for: Clinical and Pathophysiologic Insights of Free triiodothyronine/Free thyroxine Ratio in Patients with Heart Failure with Preserved Ejection Fraction: Data from the NETDiamond Cohort

description: <b><i>Background:</i></b> Thyroid dysfunction is common in patients with heart failure (HF). Impaired conversion of free T4 (FT4) into free T3 (FT3) is thought to occur in these patients, decreasing the availability of FT3 and contributing to HF progression. In HF with preserved ejection fraction (HFpEF), it is not known whether changes in conversion of thyroid hormones (THs) are associated with clinical status and outcomes. <b><i>Objectives:</i></b> The objective of this study was to evaluate the association of FT3/FT4 ratio and TH with clinical, analytical, and echocardiographic parameters, as well as their prognostic impact in individuals with stable HFpEF. <b><i>Methods:</i></b> We evaluated 74 HFpEF participants of the NETDiamond cohort without known thyroid disease. We performed regression modeling to study the associations of TH and FT3/FT4 ratio with clinical, anthropometric, analytical, and echocardiographic parameters, and survival analysis to evaluate associations with the composite of diuretic intensification, urgent HF visit, HF hospitalization, or cardiovascular death over a median follow-up of 2.8 years. <b><i>Results:</i></b> The mean age was 73.7 years and 62% were men. The mean FT3/FT4 ratio was 2.63 (standard deviation: 0.43). Subjects with lower FT3/FT4 ratio were more likely to be obese and have atrial fibrillation. Lower FT3/FT4 ratio was associated with higher body fat (β = −5.60 kg per FT3/FT4 unit, <i>p</i> = 0.034), higher pulmonary arterial systolic pressure (PASP) (β = −10.26 mm Hg per FT3/FT4 unit, <i>p</i> = 0.002), and lower left ventricular ejection fraction (LVEF) (β = 3.60% per FT3/FT4 unit, <i>p</i> = 0.008). Lower FT3/FT4 ratio was associated with higher risk for the composite HF outcome (HR = 2.50, 95% CI: 1.04–5.88, per 1-unit decrease in FT3/FT4, <i>p</i> = 0.041). <b><i>Conclusions:</i></b> In patients with HFpEF, lower FT3/FT4 ratio was associated with higher body fat, higher PASP, and lower LVEF. Lower FT3/FT4 predicted a higher risk of diuretic intensification, urgent HF visits, HF hospitalization, or cardiovascular death. These findings suggest that decreased FT4 to FT3 conversion might be a mechanism associated with HFpEF progression.
DOI: 10.6084/m9.figshare.22682191.v1
title: Supplementary Material for: Safety and Efficacy of Ticagrelor versus Clopidogrel in East Asian Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention Treated with Dual Antiplatelet Therapy: A Meta-Analysis of Randomized Controlled Trials

description: Introduction: The treatment strategy for dual antiplatelet therapy (DAPT) with ticagrelor has been controversial in East Asian patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI). Our meta-analysis aimed to demonstrate whether intensified antithrombotic regimens with ticagrelor plus aspirin have more beneficial effects and fewer adverse events compared to those of clopidogrel plus aspirin in East Asian patients with ACS undergoing PCI. Methods: We searched PubMed, Embase, Web of Science, Science Direct, Clinical Trial, Cochrane Library, and Chinese Clinical Trial Registry for randomized controlled trials (RCTs) comparing the efficacy of DAPT with ticagrelor or clopidogrel plus aspirin for secondary prevention of ACS in East Asian patients undergoing PCI. Risk ratios (RR) and 95% confidence intervals (CIs) were used as the metrics of choice for assessing treatment effect. The primary endpoint was bleeding events, and the secondary endpoints were major adverse cardiovascular and cerebrovascular events (MACCE, including cardiovascular death, non-fatal myocardial infarction, and stroke), all-cause death, and definite/probable/possible stent thrombosis. The I² index was used to assess heterogeneity. Results: Six RCTs involving a total of 2,725 patients met the inclusion criteria. The incidence of all bleeding events with ticagrelor was higher than that with clopidogrel (RR, 1.65; 95%CI, 1.31–2.07), but the incidence of MACCE was not significantly different between the two groups (RR, 1.08; 95%CI, 0.54–2.16). All-cause death (RR, 1.10; 95%CI, 0.67–1.79), cardiovascular death (RR, 1.42; 95%CI, 0.68–2.98), non-fatal MI (RR, 0.92; 95%CI, 0.48–1.78), stroke (RR,1.00; 95%CI, 0.40–2.50), and stent thrombosis (RR, 0.76; 95%CI, 0.19-2.98) were not statistically different between the two groups. Conclusion: Ticagrelor increased the risk of bleeding and did not increase treatment efficacy compared to that of clopidogrel in the East Asian population who have ACS treated with PCI.
DOI: 10.5281/zenodo.8014502
title: LargeModel

description: Some description
DOI: 10.5281/zenodo.8014501
title: LargeModel

description: Some description
DOI: 10.5281/zenodo.8014606
title: Assessing Code Readability in Python Programming Courses Using Eye-Tracking - Python Code Snippets

description: Python code snippets for assessing code readability in Python programming courses using eye-tracking.
DOI: 10.5281/zenodo.8014605
title: Assessing Code Readability in Python Programming Courses Using Eye-Tracking - Python Code Snippets

description: Python code snippets for assessing code readability in Python programming courses using eye-tracking.
DOI: 10.7294/22155401
title: Dataset: Cognitive bias in slow-growing broiler chickens raised in low- or high-complexity environments

description: Judgement bias training and testing responses Tonic immobility test responses Attention bias test responses Gait scores Feather corticosterone concentrations
DOI: 10.6084/m9.figshare.22226212.v1
title: Supplementary Material for: Association of body fat distribution and risk of breast cancer in pre- and postmenopausal women

description: Introduction Obesity is a risk factor for both the development of and mortality from breast cancer in postmenopausal but not in premenopausal women. However, which part of the fat mass is associated with risk remains unclear, and whether the difference in the risk for breast cancer is associated with discrepancy in the distribution of fat with menstrual status requires further study. Methods A dataset from the UK Biobank, which included 245,009 female participants, and 5402 females who developed breast cancer during a mean follow-up of 6.6 years was analyzed. Body fat mass was measured according to bioelectrical impedance at baseline by trained technicians. Age- and multivariable-adjusted hazard ratios and corresponding 95% confidence intervals for associations between body fat distribution and the risk for breast cancer were estimated using Cox proportional hazards regression. Height, age, education level, ethnicity, index of multiple deprivation, alcohol intake, smoking, physical activity, fruit consumption, age at menarche, age at first birth, number of births, hormone replacement therapy, family history of breast cancer, hysterectomy, and ovariotomy were adjusted for potential confounders. Results Fat distribution differed between pre- and postmenopausal women. After menopause, there was an increase in fat mass in different body segments (arms, legs, and trunk). After age- and multivariable-adjustment, fat mass in different segments, BMI, and waist circumference were significantly associated with the risk for breast cancer among postmenopausal but not premenopausal women. Conclusion Postmenopausal women exhibited more fat in different body segments, which are associated with increased risk for breast cancer, compared to premenopausal women. Fat mass control throughout the body may be beneficial in mitigating the risk for breast cancer and was not limited to abdominal fat alone among postmenopausal women.
DOI: 10.7294/22155401.v1
title: Dataset: Cognitive bias in slow-growing broiler chickens raised in low- or high-complexity environments

description: Judgement bias training and testing responses Tonic immobility test responses Attention bias test responses Gait scores Feather corticosterone concentrations
DOI: 10.25377/sussex.7969904
title: Dataset for Everyday Childhoods research project. Tempest - Favourite Things Interview (Embargo)

description: This data was collected as part of Tempest's favourite things interview, carried out on the 14/11/2013 with Mary Jane Kehily. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7969904.v1
title: Dataset for Everyday Childhoods research project. Tempest - Favourite Things Interview (Embargo)

description: This data was collected as part of Tempest's favourite things interview, carried out on the 14/11/2013 with Mary Jane Kehily. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25976/57yb-0a13
title: first pool 2020 water temp

description: The Cheticamp river's water data
DOI: 10.25377/sussex.7970120
title: Dataset for Everyday Childhoods research project. Saffron - Favourite Things Interview (embargo)

description: This data was collected as part of Saffron's favourite things interview, carried out on the 03/01/2014 with Lucy Hadfield. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7970120.v1
title: Dataset for Everyday Childhoods research project. Saffron - Favourite Things Interview (embargo)

description: This data was collected as part of Saffron's favourite things interview, carried out on the 03/01/2014 with Lucy Hadfield. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7970426
title: Dataset for Everyday Childhoods research project. Saffron - Recursive Interview (embargo)

description: This data was collected as part of Saffron's recursive interview, carried out on the 29/11/2014 with Lucy Hadfield. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7970426.v1
title: Dataset for Everyday Childhoods research project. Saffron - Recursive Interview (embargo)

description: This data was collected as part of Saffron's recursive interview, carried out on the 29/11/2014 with Lucy Hadfield. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7970192
title: Dataset for Everyday Childhoods research project. Saffron - Day in a Life (embargo)

description: This data was collected as part of Saffron's day in a life observation, carried out on the 22/05/2014 with Lucy Hadfield. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7970192.v1
title: Dataset for Everyday Childhoods research project. Saffron - Day in a Life (embargo)

description: This data was collected as part of Saffron's day in a life observation, carried out on the 22/05/2014 with Lucy Hadfield. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.34740/kaggle/dsv/5867081
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.5517/ccdc.csd.cc28ljv8
title: CCDC 2103190: Experimental Crystal Structure Determination

description: Related Article: C. Smitha Rose, T. Suthan|2022|CSD Communication|||
description: Related Article: C. Smitha Rose, T. Suthan, S. Mary Delphine, C. Cynitha Wise Bell, S. Athimoolam|2023|J.Mol.Struct.|1288|135779|doi:10.1016/j.molstruc.2023.135779
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: 10.2905/b5998c27-e829-49e0-8cbb-2ca3495d06bf
title: European market surveillance of motor vehicles - 2022 Emissions Dataset

description: Emissions dataset from on-road and laboratory testing of ligh-duty and light commercial vehicles
DOI: 10.5517/ccdc.csd.cc2d2pfm
title: CCDC 2207120: Experimental Crystal Structure Determination

description: Related Article: Meili Zhang|2022|CSD Communication|||
description: Related Article: Xue-Ying Lu, Mei-Li Zhang, Yi-Xia Ren, Ji-Jiang Wang, Xiao-Gang Yang|2023|J.Mol.Struct.|1288|135796|doi:10.1016/j.molstruc.2023.135796
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: 10.25377/sussex.7970453
title: Dataset for Everyday Childhoods research project. Nkosi - Recursive Interview (embargo)

description: This data was collected as part of Nkosi's recursive interview, carried out on the 20/10/2014 with Sue Sharpe. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.