cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.9q7zda
title: Occurrence Download

description: A dataset containing 3897 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Smilax lasioneura"
 ]
}

 The dataset includes 3897 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041681-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e6s5ys
title: Occurrence Download

description: A dataset containing 33698 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gaultheria procumbens"
 ]
}

 The dataset includes 33698 records from 270 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041680-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17075420
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250908f Initial

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250908f Initial. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.17075421
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250908f Initial

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250908f Initial. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.15468/dl.f8caz2
title: Occurrence Download

description: A dataset containing 34862 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Genista cinerea"
 ]
}

 The dataset includes 34862 records from 232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041682-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17075377
title: Integrating AI and Systems Biology for a Novel Conceptual Framework in Warm Autoimmune Hemolytic Anemia (WAIHA): Towards Predictive Diagnostics and Personalized Therapies

description: Introduction: Warm Autoimmune Hemolytic Anemia (WAIHA) presents a significant hematological challenge, characterized by unpredictable disease trajectories and variable treatment responses. Current clinical paradigms lack the predictive power to stratify patients or personalize therapies, leading to a reactive, one-size-fits-all approach.

Proposed Framework: This paper introduces a novel conceptual framework, the "AI-Driven Multi-Omics WAIHA Pathway (AD-MOWP) Model," designed to integrate advanced Artificial Intelligence (AI) with a systems biology approach. The model posits that by leveraging AI to analyze high-dimensional multi-omics data (genomics, transcriptomics, proteomics, etc.) in conjunction with rich clinical phenotypes and treatment outcomes, we can uncover previously unappreciated molecular signatures and dynamic immune dysregulations that drive WAIHA pathogenesis.

Implications and Vision: This framework promises to enable the development of highly sensitive predictive diagnostics, facilitate the stratification of patients for personalized treatment strategies, and accelerate the discovery of novel therapeutic targets for refractory disease. Ultimately, the AD-MOWP Model represents a conceptual leap from reactive management to proactive precision medicine, offering a robust theoretical foundation to revolutionize the care of patients with WAIHA and enhance human welfare.
DOI: 10.5281/zenodo.17075378
title: Integrating AI and Systems Biology for a Novel Conceptual Framework in Warm Autoimmune Hemolytic Anemia (WAIHA): Towards Predictive Diagnostics and Personalized Therapies

description: Introduction: Warm Autoimmune Hemolytic Anemia (WAIHA) presents a significant hematological challenge, characterized by unpredictable disease trajectories and variable treatment responses. Current clinical paradigms lack the predictive power to stratify patients or personalize therapies, leading to a reactive, one-size-fits-all approach.

Proposed Framework: This paper introduces a novel conceptual framework, the "AI-Driven Multi-Omics WAIHA Pathway (AD-MOWP) Model," designed to integrate advanced Artificial Intelligence (AI) with a systems biology approach. The model posits that by leveraging AI to analyze high-dimensional multi-omics data (genomics, transcriptomics, proteomics, etc.) in conjunction with rich clinical phenotypes and treatment outcomes, we can uncover previously unappreciated molecular signatures and dynamic immune dysregulations that drive WAIHA pathogenesis.

Implications and Vision: This framework promises to enable the development of highly sensitive predictive diagnostics, facilitate the stratification of patients for personalized treatment strategies, and accelerate the discovery of novel therapeutic targets for refractory disease. Ultimately, the AD-MOWP Model represents a conceptual leap from reactive management to proactive precision medicine, offering a robust theoretical foundation to revolutionize the care of patients with WAIHA and enhance human welfare.
DOI: 10.15468/dl.za95j7
title: Occurrence Download

description: A dataset containing 233247 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plectorhinchus gaterinus, Plectorhinchus lessonii, Plectorhinchus lineatus, Plectorhinchus macrospilus, Plectorhinchus mediterraneus, Plectorhinchus paulayi, Plectorhinchus plagiodesmus, Plectorhinchus playfairi, Plectorhinchus polytaenia, Plectorhinchus sordidus, Plectorhinchus vittatus, Plectranthias altipinnatus, Plectranthias bauchotae, Plectranthias cirrhitoides, Plectranthias elaine, Plectranthias flammeus, Plectranthias fourmanoiri, Plectranthias gardineri, Plectranthias helenae, Plectranthias inermis, Plectranthias knappi, Plectranthias longimanus, Plectranthias nanus, Plectranthias pelicieri, Plectranthias retrofasciatus, Plectranthias rubrifasciatus, Plectranthias taylori, Plectranthias vexillarius, Plectranthias winniensis, Plectranthias yamakawai, Plectroglyphidodon aureus, Plectroglyphidodon dickii, Plectroglyphidodon fasciolatus, Plectroglyphidodon imparipennis, Plectroglyphidodon insularis, Plectroglyphidodon johnstonianus, Plectroglyphidodon leucozonus, Plectroglyphidodon obreptus, Plectroglyphidodon phoenixensis, Plectropomus areolatus, Plectropomus laevis, Plectropomus leopardus, Plectropomus maculatus, Plectropomus marisrubri, Plectropomus oligacanthus, Plectropomus pessuliferus, Plectropomus punctatus, Plectrypops lima, Plectrypops retrospinis, Plesiops cephalotaenia, Plesiops coeruleolineatus, Plesiops corallicola, Plesiops malalaxus, Plesiops multisquamata, Plesiops mystaxus, Plesiops nigricans, Plesiops oxycephalus, Pleuronichthys cornutus, Pleuronichthys ocellatus, Pleuronichthys ritteri, Pleuronichthys verticalis, Pleurosicya annandalei, Pleurosicya australis, Pleurosicya boldinghi, Pleurosicya carolinensis, Pleurosicya coerulea, Pleurosicya elongata, Pleurosicya fringilla, Pleurosicya labiata, Pleurosicya larsonae, Pleurosicya micheli, Pleurosicya mossambica, Pleurosicya muscarum, Pleurosicya occidentalis, Pleurosicya plicata, Pleurosicya prognatha, Pleurosicya spongicola, Pliosteostoma lutipinnis, Pogonoperca ocellata, Pogonoscorpius sechellensis, Pollachius pollachius, Polyamblyodon germanum, Polyamblyodon gibbosum, Polydactylus octonemus, Polydactylus persicus, Polyipnus surugaensis, Polylepion cruentum, Polyplacapros tyleri, Polyspina piosae, Polysteganus baissaci, Polysteganus cerasinus, Polysteganus praeorbitalis, Polysteganus undulosus, Pomacanthus annularis, Pomacanthus arcuatus, Pomacanthus asfur, Pomacanthus chrysurus, Pomacanthus imperator, Pomacanthus maculosus, Pomacanthus navarchus)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 233247 records from 518 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041644-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ujqbs6
title: Occurrence Download

description: A dataset containing 137777 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pomacanthus paru, Pomacanthus rhomboides, Pomacanthus semicirculatus, Pomacanthus sexstriatus, Pomacanthus xanthometopon, Pomacanthus zonipectus, Pomacentrus adelus, Pomacentrus albicaudatus, Pomacentrus alexanderae, Pomacentrus alleni, Pomacentrus amboinensis, Pomacentrus arabicus, Pomacentrus armillatus, Pomacentrus auriventris, Pomacentrus australis, Pomacentrus azuremaculatus, Pomacentrus bankanensis, Pomacentrus burroughi, Pomacentrus caeruleus, Pomacentrus callainus, Pomacentrus cheraphilus, Pomacentrus chrysurus, Pomacentrus coelestis, Pomacentrus cuneatus, Pomacentrus geminospilus, Pomacentrus grammorhynchus, Pomacentrus imitator, Pomacentrus indicus, Pomacentrus lepidogenys, Pomacentrus melanochir, Pomacentrus milleri, Pomacentrus moluccensis, Pomacentrus nagasakiensis, Pomacentrus nigromanus, Pomacentrus nigromarginatus, Pomacentrus opisthostigma, Pomacentrus pavo, Pomacentrus philippinus, Pomacentrus pikei, Pomacentrus reidi, Pomacentrus similis, Pomacentrus simsiang, Pomacentrus smithi, Pomacentrus stigma, Pomacentrus sulfureus, Pomacentrus trichrourus, Pomacentrus trilineatus, Pomacentrus vaiuli, Pomacentrus wardi, Pomacentrus xanthosternus, Pomachromis fuscidorsalis, Pomachromis guamensis, Pomachromis richardsoni, Pomadasys aheneus, Pomadasys furcatus, Pomadasys laurentino, Pomadasys punctulatus, Pomadasys quadrilineatus, Pomadasys schyrii, Pomadasys striatus, Pomadasys stridens, Pomadasys suillus, Pomadasys taeniatus, Pomadasys unimaculatus, Pomatoschistus knerii, Pomatoschistus nanus, Pomatoschistus pictus, Pomatoschistus quagga, Ponticola cephalargoides, Pontinus clemensi, Pontinus nigerimum, Pontinus nigropunctatus, Pontinus rathbuni, Pontinus strigatus, Pontinus vaughani, Porcostoma dentata, Porichthys greenei, Porichthys kymosemeum, Porichthys margaritatus, Porichthys mimeticus, Porichthys myriaster, Porichthys oculellus, Porichthys oculofrenum, Porichthys pauciradiatus, Porichthys plectrodon, Porichthys porosissimus, Porocephalichthys dasyrhynchus, Posidonichthys hutchinsi, Powellichthys ventriosus, Praealticus bilineatus, Praealticus caesius, Praealticus labrovittatus, Praealticus margaritarius, Praealticus margaritatus, Praealticus multistriatus, Praealticus natalis, Praealticus oortii, Praealticus poptae, Praealticus striatus, Praealticus tanegasimae)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 137777 records from 463 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041645-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j5yssb
title: Occurrence Download

description: A dataset containing 742 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Olpogastra lugubris, Onychogomphus, Onychogomphus aequistylus, Onychogomphus boudoti, Onychogomphus costae, Onychogomphus forcipatus, Onychogomphus forcipatus unguiculatus, Onychogomphus kitchingmani, Onychogomphus lefebvrii, Onychogomphus nigrotibialis, Onychogomphus pilosus, Onychogomphus rossii, Onychogomphus seydeli, Onychogomphus styx, Onychogomphus supinus, Onychogomphus uncatus, Onychogomphus undecim, Onychogomphus vadoni, Ophiogomphus, Oreocnemis, Oreocnemis phoenix, Orthetrum, Orthetrum abbotti, Orthetrum africanum, Orthetrum agaricum, Orthetrum angustiventre, Orthetrum austeni, Orthetrum azureum, Orthetrum azureum lugubre, Orthetrum brachiale, Orthetrum brevistylum, Orthetrum brunneum, Orthetrum caffrum, Orthetrum camerunense, Orthetrum cancellatum, Orthetrum chrysostigma, Orthetrum coerulescens, Orthetrum coerulescens anceps, Orthetrum guineense, Orthetrum hintzi, Orthetrum icteromelas, Orthetrum julia, Orthetrum julia falsum, Orthetrum julia julia, Orthetrum kafwi, Orthetrum kollmannspergeri, Orthetrum kristenseni, Orthetrum latihami, Orthetrum lemur, Orthetrum lugubre, Orthetrum lusinga, Orthetrum machadoi, Orthetrum macrostigma, Orthetrum malgassicum, Orthetrum microstigma, Orthetrum monardi, Orthetrum nitidinerve, Orthetrum pruinosum, Orthetrum ransonnetii, Orthetrum robustum, Orthetrum rubens, Orthetrum sabina, Orthetrum sabina sabina, Orthetrum saegeri, Orthetrum sagitta, Orthetrum, Orthetrum stemmale, Orthetrum trinacria, Orthetrum umbratum, Oxygastra, Oxygastra curtisii, Oxythemis, Oxythemis phoenicosceles, Palpopleura, Palpopleura albifrons, Palpopleura deceptor, Palpopleura jucunda, Palpopleura lucia, Palpopleura portia, Palpopleura vestita, Pantala, Pantala flavescens, Paracnemis, Paracnemis alluaudi, Paragomphus, Paragomphus abnormis, Paragomphus acuminatus, Paragomphus alluaudi, Paragomphus aureatus, Paragomphus bredoi, Paragomphus cammaertsi, Paragomphus cataractae, Paragomphus clausnitzerorum, Paragomphus cognatus, Paragomphus crenigomphoides, Paragomphus darwalli, Paragomphus dispar, Paragomphus elpidius, Paragomphus fritillarius, Paragomphus genei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.9094 1.1982,46.1382 1.9309,46.1382 2.0599,37.1382 2.0599,37.1382 -6.9401,40.3593 -6.9401,40.412 -6.7736,40.0778 -6.426,39.9615 -5.9146,40.3471 -5.2827,40.3421 -4.7259,40.0631 -4.4307,40.3789 -3.7221,40.7318 -3.0429,41.8321 -2.1586,43.8394 0.2906,44.9094 1.1982))"
 ]
}

 The dataset includes 742 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041687-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2xda54
title: Occurrence Download

description: A dataset containing 1480 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Smilax glaucochina"
 ]
}

 The dataset includes 1480 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041688-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yba5cz
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phyla nodiflora",
 "Country is Türkiye",
 "HasCoordinate is true"
 ]
}

 The dataset includes 54 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041689-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17075251
title: A constructed long-term (1950-2014) runoff dataset in Balkhash Lake basin

description:      We collected gauge runoff data from 13 hydrological stations  in all five rivers in the BLB from the hydrological yearbook and literature. Seven stations are located in the Ili River: the Ushjarma (1939–1985), Kapchagay (1930–2000), Dobyn (1929–2014), and Yamate (1949–1986) stations in the mainstream, and three stations including the Tekes (1956–1979), Kunes (1960–1979), and Kashi (1953–2015) stations in the mountainous reaches (the Tekes, Kunes, and Kashi Rivers). For the East Rivers, two stations are located in the Karatal River: the downstream Ushtobe station (1939–1985) and the upstream Tekeli station (1940–1985). Two stations are located in the Lepsy River: the downstream Lepsy station (1936–1985) and the upstream Lepsinsk station (1936–1985). Runoff data were acquired from the upstream Chann station (1936–1981) in the Aksu River and from the downstream Ayaguz station (1949–1986) in the Ayaguz River.

    To construct a long-term annual runoff series for all 13 sub-basins, we first developed linear regression models relating observed gauge runoff (R) to the corresponding yearly aggregated gridded runoff from both GLDAS-2.0 (RGLDAS) and ERA5-Land (RERA5-Land) within their common period of overlap with the gauge data, as formulated in Equation 1 (where a₁, a₂, and R₀ are regression coefficients). The regression equations were then used to infill missing gauge-recorded runoff values based on RGLDAS and RERA5-Land inputs, thereby reconstructing a continuous runoff series. We determined that combining both GLDAS-2.0 and ERA5-Land products yielded more accurate runoff reconstructions than using either product alone. Since the overlapping period of GLDAS-2.0 and ERA5-Land spans from 1950 to 2014, we reconstructed runoff over this interval to maximize reliability.
DOI: 10.5281/zenodo.17075252
title: A constructed long-term (1950-2014) runoff dataset in Balkhash Lake basin

description:      We collected gauge runoff data from 13 hydrological stations  in all five rivers in the BLB from the hydrological yearbook and literature. Seven stations are located in the Ili River: the Ushjarma (1939–1985), Kapchagay (1930–2000), Dobyn (1929–2014), and Yamate (1949–1986) stations in the mainstream, and three stations including the Tekes (1956–1979), Kunes (1960–1979), and Kashi (1953–2015) stations in the mountainous reaches (the Tekes, Kunes, and Kashi Rivers). For the East Rivers, two stations are located in the Karatal River: the downstream Ushtobe station (1939–1985) and the upstream Tekeli station (1940–1985). Two stations are located in the Lepsy River: the downstream Lepsy station (1936–1985) and the upstream Lepsinsk station (1936–1985). Runoff data were acquired from the upstream Chann station (1936–1981) in the Aksu River and from the downstream Ayaguz station (1949–1986) in the Ayaguz River.

    To construct a long-term annual runoff series for all 13 sub-basins, we first developed linear regression models relating observed gauge runoff (R) to the corresponding yearly aggregated gridded runoff from both GLDAS-2.0 (RGLDAS) and ERA5-Land (RERA5-Land) within their common period of overlap with the gauge data, as formulated in Equation 1 (where a₁, a₂, and R₀ are regression coefficients). The regression equations were then used to infill missing gauge-recorded runoff values based on RGLDAS and RERA5-Land inputs, thereby reconstructing a continuous runoff series. We determined that combining both GLDAS-2.0 and ERA5-Land products yielded more accurate runoff reconstructions than using either product alone. Since the overlapping period of GLDAS-2.0 and ERA5-Land spans from 1950 to 2014, we reconstructed runoff over this interval to maximize reliability.
DOI: 10.17909/rxm9-qd05
title: JWST NIRSpec spectroscopy and MIRI photometry for WISEP J075108.79-763449
DOI: 10.15468/dl.5v8s68
title: Occurrence Download

description: A dataset containing 4977835 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "GadmGid is one of (COL.2_2, COL.2.9_2)"
 ]
}

 The dataset includes 4977835 records from 855 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041616-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30071872
title: Enhancing Carbon Restoration and Ecosystem Resilience in Global Drylands via Water-to-Carbon Biotransformation Strategies

description: The Earth's terrestrial carbon stocks have depleted an estimated 344 billion tons. In vulnerable drylands, restoring carbon sinks requires optimizing water-to-carbon biotransformation amid water scarcity. Using second-order meta-analysis, we identify key biophysical pathways for carbon restoration and ecosystem resilience: (i) Cropping diversification increases net primary productivity by 18.9% (<i>n</i> = 1,296 studies); (ii) Regulated deficit irrigation cuts water use by 30–50% while improving water use efficiency by 3.4% (<i>n</i> = 9,068); (iii) Soil mulching increases land productivity by 22.2% (<i>n</i> = 48,144); (iv) Soil health rejuvenation strategies sequester 1.2–3.8 t SOC ha⁻¹ yr⁻¹. Priorities to implement these biophysical pathways include water-smart irrigation, CO<sub>2</sub> fertilization-enhanced photosynthetic assimilation, rhizosphere engineering for microbiome-based nutrient solutions, bio-mulches replacing traditional plastic films, diversified farming systems with low soil disturbance, and inclusive governance frameworks. These strategies reconcile water scarcity with carbon restoration to enhance dryland ecosystem resilience and support the UN's Sustainable Development Goals.
DOI: 10.6084/m9.figshare.30071872.v2
title: Enhancing Carbon Restoration and Ecosystem Resilience in Global Drylands via Water-to-Carbon Biotransformation Strategies

description: The Earth's terrestrial carbon stocks have depleted an estimated 344 billion tons. In vulnerable drylands, restoring carbon sinks requires optimizing water-to-carbon biotransformation amid water scarcity. Using second-order meta-analysis, we identify key biophysical pathways for carbon restoration and ecosystem resilience: (i) Cropping diversification increases net primary productivity by 18.9% (<i>n</i> = 1,296 studies); (ii) Regulated deficit irrigation cuts water use by 30–50% while improving water use efficiency by 3.4% (<i>n</i> = 9,068); (iii) Soil mulching increases land productivity by 22.2% (<i>n</i> = 48,144); (iv) Soil health rejuvenation strategies sequester 1.2–3.8 t SOC ha⁻¹ yr⁻¹. Priorities to implement these biophysical pathways include water-smart irrigation, CO<sub>2</sub> fertilization-enhanced photosynthetic assimilation, rhizosphere engineering for microbiome-based nutrient solutions, bio-mulches replacing traditional plastic films, diversified farming systems with low soil disturbance, and inclusive governance frameworks. These strategies reconcile water scarcity with carbon restoration to enhance dryland ecosystem resilience and support the UN's Sustainable Development Goals.
DOI: 10.6084/m9.figshare.30071872.v1
title: Enhancing Carbon Restoration and Ecosystem Resilience in Global Drylands via Water-to-Carbon Biotransformation Strategies

description: The Earth's terrestrial carbon stocks have depleted an estimated 344 billion tons. In vulnerable drylands, restoring carbon sinks requires optimizing water-to-carbon biotransformation amid water scarcity. Using second-order meta-analysis, we identify key biophysical pathways for carbon restoration and ecosystem resilience: (i) Cropping diversification increases net primary productivity by 18.9% (<i>n</i> = 1,296 studies); (ii) Regulated deficit irrigation cuts water use by 30–50% while improving water use efficiency by 3.4% (<i>n</i> = 9,068); (iii) Soil mulching increases land productivity by 22.2% (<i>n</i> = 48,144); (iv) Soil health rejuvenation strategies sequester 1.2–3.8 t SOC ha⁻¹ yr⁻¹. Priorities to implement these biophysical pathways include water-smart irrigation, CO<sub>2</sub> fertilization-enhanced photosynthetic assimilation, rhizosphere engineering for microbiome-based nutrient solutions, bio-mulches replacing traditional plastic films, diversified farming systems with low soil disturbance, and inclusive governance frameworks. These strategies reconcile water scarcity with carbon restoration to enhance dryland ecosystem resilience and support the UN's Sustainable Development Goals.